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

A

AbstractAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation

Abstract base class for objects capable of annotating things.

AbstractAnnotator() - Constructor for class org.openimaj.ml.annotation.AbstractAnnotator

AbstractContextAwareInitStrategy<INDEPENDANT,DEPENDANT> - Class in org.openimaj.ml.linear.learner.init

Holds on to the learner and the context variables

AbstractContextAwareInitStrategy() - Constructor for class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

add(long, DATA) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

Add an element to this time series

addDocument(Document) - Method in class org.openimaj.pgm.util.Corpus

addIteration(String, Iterable<T>) - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearLearnerParametersLineSearch

addTimeSeries(String, INTERNALSERIES) - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

addTimeSeries(String, INTERNALSERIES) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

addU(int) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

Expand the U parameters matrix by added a set of rows.

addU(int) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

Expand the U parameters matrix by added a set of rows.

addW(int) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

Expand the W parameters matrix by added a set of rows.

addW(int) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

Expand the W parameters matrix by added a set of rows.

aggregate(DoubleSynchronisedTimeSeriesCollection) - Method in class org.openimaj.ml.timeseries.aggregator.MeanSquaredDifferenceAggregator

aggregate(DoubleSynchronisedTimeSeriesCollection) - Method in class org.openimaj.ml.timeseries.aggregator.SquaredSummedDifferenceAggregator

aggregate(STSCOLLECTION) - Method in interface org.openimaj.ml.timeseries.aggregator.SynchronisedTimeSeriesCollectionAggregator

aggregate(TSCOLLECTION) - Method in interface org.openimaj.ml.timeseries.aggregator.TimeSeriesCollectionAggregator

aggregate(DoubleSynchronisedTimeSeriesCollection) - Method in class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

allseries() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

allseries() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

alpha - Variable in class org.openimaj.pgm.vb.lda.mle.LDAModel

the dirichelet perameter for every dimension of the topic multinomial prior

annotate(OBJECT) - Method in interface org.openimaj.ml.annotation.Annotator

Generate annotations for the given object.

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.linear.LiblinearAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.linear.LinearSVMAnnotator

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.model.ModelAnnotator

annotate(OBJECT, Collection<ANNOTATION>) - Method in interface org.openimaj.ml.annotation.RestrictedAnnotator

Generate annotations for the given object, restricting the potential annotations to coming from the given set.

annotate(OBJECT) - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Generate annotations for the given object.

Annotated<OBJECT,ANNOTATION> - Interface in org.openimaj.ml.annotation

An object with annotations.

AnnotatedListHelper<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.utils

Helper class for dealing with lists of annotated objects, and specifically getting objects by class and determining the set of annotations.

AnnotatedListHelper(List<? extends Annotated<OBJECT, ANNOTATION>>) - Constructor for class org.openimaj.ml.annotation.utils.AnnotatedListHelper

Construct the AnnotatedListHelper with the given list.

AnnotatedObject<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation

Basic implementation of Annotated.

AnnotatedObject(OBJECT, Collection<ANNOTATION>) - Constructor for class org.openimaj.ml.annotation.AnnotatedObject

Construct with the given object and its annotations.

AnnotatedObject(OBJECT, ANNOTATION) - Constructor for class org.openimaj.ml.annotation.AnnotatedObject

Construct with the given object and its annotation.

annotation - Variable in class org.openimaj.ml.annotation.ScoredAnnotation

The annotation

AnnotationEvaluator<OBJECT extends org.openimaj.data.identity.Identifiable,ANNOTATION> - Class in org.openimaj.ml.annotation.evaluation

A class to help evaluate the performance of an Annotator using standardised classification and/or retrieval evaluation methodologies.

AnnotationEvaluator(Annotator<OBJECT, ANNOTATION>, Dataset<? extends Annotated<OBJECT, ANNOTATION>>) - Constructor for class org.openimaj.ml.annotation.evaluation.AnnotationEvaluator

Construct a new AnnotationEvaluator with the given annotator and test data (with ground-truth annotations).

annotationProbability - Variable in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

annotations - Variable in class org.openimaj.ml.annotation.AnnotatedObject

The annotations

annotations - Variable in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

annotations - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

annotations - Variable in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

annotationsSet - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

Annotator<OBJECT,ANNOTATION> - Interface in org.openimaj.ml.annotation

Base class for objects capable of annotating things.

apply(IndependentPair<double[], double[]>) - Method in class org.openimaj.ml.linear.kernel.LinearVectorKernel

ArrayIndexComparator - Class in org.openimaj.ml.linear.experiments.sinabill

ArrayIndexComparator(double[]) - Constructor for class org.openimaj.ml.linear.experiments.sinabill.ArrayIndexComparator

asciiHeader() - Method in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

asciiHeader() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

asciiHeader() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

asMatrixPair(IndependentPair<Map<String, Map<String, Double>>, Map<String, Double>>, int, int, int) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

Given a sparse pair of user/words and value construct a pair of matricies using the current mappings of words and users to matrix rows.

asMatrixPair(IndependentPair<Map<String, Map<String, Double>>, Map<String, Double>>) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

asMatrixPair(Map<String, Map<String, Double>>, Map<String, Double>) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

AustrianWordExperiments - Class in org.openimaj.ml.linear.experiments.sinabill

AustrianWordExperiments() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.AustrianWordExperiments

B

b - Variable in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

BatchAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation

An Annotator that is trained in "batch" mode; all training examples are presented at once.

BatchAnnotator() - Constructor for class org.openimaj.ml.annotation.BatchAnnotator

BatchTrainer<T> - Interface in org.openimaj.ml.training

Interface describing objects capable of performing training in "batch" mode; all training examples are presented at once.

BIAS - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

whether a bias component is added to w and u.

bias - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

bias - Variable in class org.openimaj.ml.linear.learner.loss.LossFunction

bias - Variable in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

bias - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

BIASINITSTRAT - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The initialisation strategy for BIAS.

biasMode - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

biasMode - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

BICONVEX_MAXITER - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The maximum number of iterations in the biconvex iterative stage.

BICONVEX_TOL - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The threshold of the ratio between the (sum(new_w - old_w) + sum(new_u - old_u)) / (sum(old_u) + sum(old_w)) i.e.

BiconvexDataGenerator - Class in org.openimaj.ml.linear.data

Data generated from a biconvex system of the form: Y_n,t = U_:,t^T .

BiconvexDataGenerator() - Constructor for class org.openimaj.ml.linear.data.BiconvexDataGenerator

Generates a biconvex data generator.

BiconvexDataGenerator(int, int, int, double, double, boolean, boolean, int, double) - Constructor for class org.openimaj.ml.linear.data.BiconvexDataGenerator

BiconvexIncrementalDataGenerator - Class in org.openimaj.ml.linear.data

BiconvexIncrementalDataGenerator(int, int, int, double, double, boolean, boolean, int, double) - Constructor for class org.openimaj.ml.linear.data.BiconvexIncrementalDataGenerator

BilinearEvaluator - Class in org.openimaj.ml.linear.evaluation

BilinearEvaluator() - Constructor for class org.openimaj.ml.linear.evaluation.BilinearEvaluator

BilinearExperiment - Class in org.openimaj.ml.linear.experiments.sinabill

BilinearExperiment() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

BilinearLearnerParameters - Class in org.openimaj.ml.linear.learner

Parameters used to control a BilinearSparseOnlineLearner

BilinearLearnerParameters() - Constructor for class org.openimaj.ml.linear.learner.BilinearLearnerParameters

sets up the defaults

BilinearLearnerParametersLineSearch - Class in org.openimaj.ml.linear.experiments.sinabill

BilinearLearnerParametersLineSearch(BilinearLearnerParameters) - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BilinearLearnerParametersLineSearch

Set the base paramters.

BilinearSparseFISTALearner - Class in org.openimaj.ml.linear.learner

BilinearSparseFISTALearner(BilinearLearnerParameters) - Constructor for class org.openimaj.ml.linear.learner.BilinearSparseFISTALearner

BilinearSparseFISTALearner() - Constructor for class org.openimaj.ml.linear.learner.BilinearSparseFISTALearner

BilinearSparseOnlineLearner - Class in org.openimaj.ml.linear.learner

An implementation of a stochastic gradient decent with proximal perameter adjustment (for regularised parameters).

BilinearSparseOnlineLearner() - Constructor for class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

The default parameters.

BilinearSparseOnlineLearner(BilinearLearnerParameters) - Constructor for class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

BilinearUnmixedSparseOnlineLearner - Class in org.openimaj.ml.linear.learner

An implementation of a stochastic gradient decent with proximal parameter adjustment (for regularised parameters).

BilinearUnmixedSparseOnlineLearner() - Constructor for class org.openimaj.ml.linear.learner.BilinearUnmixedSparseOnlineLearner

BillAustrianDampeningExperiments - Class in org.openimaj.ml.linear.experiments.sinabill

BillAustrianDampeningExperiments() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BillAustrianDampeningExperiments

BillAustrianExperiments - Class in org.openimaj.ml.linear.experiments.sinabill

BillAustrianExperiments() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperiments

BillAustrianExperimentsNormalised - Class in org.openimaj.ml.linear.experiments.sinabill

BillAustrianExperimentsNormalised() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperimentsNormalised

BillMatlabFileDataGenerator - Class in org.openimaj.ml.linear.data

Read data from bill's matlab file format

BillMatlabFileDataGenerator(File, int, boolean) - Constructor for class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

BillMatlabFileDataGenerator(File, String, File, int, boolean) - Constructor for class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

BillMatlabFileDataGenerator(File, String, File, int, boolean, List<BillMatlabFileDataGenerator.Fold>) - Constructor for class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

BillMatlabFileDataGenerator.Fold - Class in org.openimaj.ml.linear.data

BillMatlabFileDataGenerator.Mode - Enum in org.openimaj.ml.linear.data

The modes

BillWordInvestigation - Class in org.openimaj.ml.linear.experiments.sinabill

BillWordInvestigation() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.BillWordInvestigation

binaryHeader() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

binaryHeader() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

bLearnRate - Variable in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

C

calculateError(List<? extends IndependentPair<double[], Integer>>) - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

classify(OBJECT) - Method in class org.openimaj.ml.annotation.AbstractAnnotator

classify(double[], double[]) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Determine if two features are from the same class or different classes.

classMap - Variable in class org.openimaj.ml.annotation.svm.SVMAnnotator

Stores the mapping between the positive and negative class and the annotation

clone() - Method in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

clone() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

clone() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

clone() - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

clone() - Method in class org.openimaj.ml.regression.LinearRegression

collectionByNames(String...) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

collectionByNames(Collection<String>) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

comparator - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

compare(Integer, Integer) - Method in class org.openimaj.ml.linear.experiments.sinabill.ArrayIndexComparator

compareTo(ScoredAnnotation<ANNOTATION>) - Method in class org.openimaj.ml.annotation.ScoredAnnotation

computeHyperplanePoint(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

Compute NaN-coordinate of a point on the hyperplane given non-NaN-coordinates.

computeLikelihood(Document, LDAVariationlState) - Method in class org.openimaj.pgm.vb.lda.mle.LDALearner

Calculates a lower bound for the log liklihood of a document given current parameters.

ConcreteTimeSeries<DATA,TS extends ConcreteTimeSeries<DATA,TS>> - Class in org.openimaj.ml.timeseries.series

A generic though inefficient time series which can be used by any data type.

ConcreteTimeSeries() - Constructor for class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

Initialise the backing treemap

confidence - Variable in class org.openimaj.ml.annotation.ScoredAnnotation

The confidence of the annotation

ContextAwareInitStrategy<INDEPENDANT,DEPENDANT> - Interface in org.openimaj.ml.linear.learner.init

A ContextAwareInitStrategy is told the learner it is initialising against and the current INDEPENDANT and DEPENDANT variables at init time.

convert(FeatureVector, double) - Static method in class org.openimaj.ml.annotation.utils.LiblinearHelper

Convert a FeatureVector to an array of Features.

convert(INPUTTS) - Method in class org.openimaj.ml.timeseries.converter.DoubleProviderTimeSeriesConverter

convert(INPUTTS, TimeSeriesProcessor<double[], Double, DoubleTimeSeries>) - Method in class org.openimaj.ml.timeseries.converter.DoubleProviderTimeSeriesConverter

convert(INPUTTS) - Method in interface org.openimaj.ml.timeseries.converter.TimeSeriesConverter

convert(INPUTTS, TimeSeriesProcessor<OUTPUTALL, OUTPUTSINGLE, OUTPUTTS>) - Method in interface org.openimaj.ml.timeseries.converter.TimeSeriesConverter

convert and process a time series

convert(TimeSeriesConverter<DATA, SINGLE_TYPE, RETURNTYPE, OUTDATA, OUTSING, OUTRET>) - Method in class org.openimaj.ml.timeseries.TimeSeries

Convert a TimeSeries

convert(TimeSeriesConverter<DATA, SINGLE_TYPE, RETURNTYPE, OUTDATA, OUTSING, OUTRET>, TimeSeriesProcessor<OUTDATA, OUTSING, OUTRET>) - Method in class org.openimaj.ml.timeseries.TimeSeries

Convert a TimeSeries

convertDense(FeatureVector, double) - Static method in class org.openimaj.ml.annotation.utils.LiblinearHelper

Convert a FeatureVector to an array of doubles using FeatureVector.asDoubleVector().

convertInternal(TimeSeriesConverter<ALLINPUT, SINGLEINPUT, INTERNALSERIES, OUTPUTALL, OUTPUTSINGLE, OUTPUTTS>, OUTPUTTSC) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

convertInternal(TimeSeriesConverter<ALLINPUT, SINGLEINPUT, INTERNALSERIES, OUTPUTALL, OUTPUTSINGLE, OUTPUTTS>, TimeSeriesProcessor<OUTPUTALL, OUTPUTSINGLE, OUTPUTTS>, OUTPUTTSC) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

convolveHorizontal(double[], double[]) - Static method in class org.openimaj.ml.timeseries.processor.GaussianTimeSeriesProcessor

Convolve a double array

copy() - Method in class org.openimaj.ml.timeseries.TimeSeries

Corpus - Class in org.openimaj.pgm.util

A corpus holds a list of documents

Corpus(int) - Constructor for class org.openimaj.pgm.util.Corpus

CorpusReader - Interface in org.openimaj.pgm.util

correct(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron

correct(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

correct(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.PlusOneDoubleArrayKernelPerceptron

countUniqueWords() - Method in class org.openimaj.pgm.util.Document

create(OBJECT, Collection<ANNOTATION>) - Static method in class org.openimaj.ml.annotation.AnnotatedObject

Create an AnnotatedObject with the given object and its annotations.

create(OBJECT, ANNOTATION) - Static method in class org.openimaj.ml.annotation.AnnotatedObject

Create an AnnotatedObject with the given object and its annotation.

create(EXTRACTOR, DistanceComparator<FEATURE>, float) - Static method in class org.openimaj.ml.annotation.basic.KNNAnnotator

Create a new KNNAnnotator with the given extractor, comparator and threshold.

create(EXTRACTOR, DistanceComparator<FEATURE>) - Static method in class org.openimaj.ml.annotation.basic.KNNAnnotator

Create a new KNNAnnotator with the given extractor and comparator.

create(EXTRACTOR, DistanceComparator<FEATURE>, int) - Static method in class org.openimaj.ml.annotation.basic.KNNAnnotator

Create a new KNNAnnotator with the given extractor, comparator and number of neighbours.

create(EXTRACTOR, DistanceComparator<FEATURE>, int, float) - Static method in class org.openimaj.ml.annotation.basic.KNNAnnotator

Create a new KNNAnnotator with the given extractor, comparator, number of neighbours and threshold.

create(NaiveBayesAnnotator.Mode) - Static method in class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

Convenience method to construct a NaiveBayesAnnotator in the case where the raw objects are themselves the feature and thus an IdentityFeatureExtractor can be used.

createList(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT>) - Static method in class org.openimaj.ml.annotation.AnnotatedObject

Convert a grouped dataset to a list of annotated objects.

createList(OBJECT[], ANNOTATION[]) - Static method in class org.openimaj.ml.annotation.AnnotatedObject

Convert parallel arrays of objects and annotations to a list of AnnotatedObject.

createList(OBJECT[], ANNOTATION[][]) - Static method in class org.openimaj.ml.annotation.AnnotatedObject

Convert parallel arrays of objects and annotations to a list of AnnotatedObject.

createWrappedExtractor(FeatureExtractor<? extends FeatureVector, T>) - Method in class org.openimaj.ml.kernel.HomogeneousKernelMap

Construct a new HomogeneousKernelMap.ExtractorWrapper that applies the map to features extracted by an internal extractor.

crossValidation(List<? extends Annotated<OBJECT, ANNOTATION>>, int) - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Performs cross-validation on the SVM.

crossValidation(svm_problem, svm_parameter, int) - Static method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Performs cross-validation on the SVM.

CurrentUMean - Class in org.openimaj.ml.linear.learner.init

Useable only with BilinearSparseOnlineLearner instances.

CurrentUMean() - Constructor for class org.openimaj.ml.linear.learner.init.CurrentUMean

CurrentValueMean - Class in org.openimaj.ml.linear.learner.init

Given a matrix considered its "current value" this init strategy takes the current value and averages the columns (creating the mean row).

CurrentValueMean() - Constructor for class org.openimaj.ml.linear.learner.init.CurrentValueMean

CurrentWMean - Class in org.openimaj.ml.linear.learner.init

Useable only with BilinearSparseOnlineLearner instances.

CurrentWMean() - Constructor for class org.openimaj.ml.linear.learner.init.CurrentWMean

D

DAMPENING - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The value of w, u and beta are updated each time data is added s.t.

DATA_ROOT() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

dataFromRoot(String) - Static method in class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

DataGenerator<I,D> - Interface in org.openimaj.ml.linear.data

DEFAULT_GAUSS_TRUNCATE - Static variable in class org.openimaj.ml.timeseries.processor.GaussianTimeSeriesProcessor

The default number of sigmas at which the Gaussian function is truncated when building a kernel

DenseLinearTransformAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.linear

An annotator that determines a "transform" between feature vectors and vectors of annotation counts.

DenseLinearTransformAnnotator(int, FeatureExtractor<? extends FeatureVector, OBJECT>) - Constructor for class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

Construct with the given number of dimensions and feature extractor.

diagX - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

diagX - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

digamma - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

Holds the first derivative of the gamma

Document - Class in org.openimaj.pgm.util

A document is a bag of words

Document(Corpus) - Constructor for class org.openimaj.pgm.util.Document

Document(int) - Constructor for class org.openimaj.pgm.util.Document

DoubleArrayKernelPerceptron - Class in org.openimaj.ml.linear.learner.perceptron

An implementation of a simple KernelPerceptron which works with double arrays.

DoubleArrayKernelPerceptron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

DoubleProviderTimeSeriesConverter<INPUTALL,INPUTSINGLE,INPUTTS extends TimeSeries<INPUTALL,INPUTSINGLE,INPUTTS> & DoubleTimeSeriesProvider> - Class in org.openimaj.ml.timeseries.converter

DoubleProviderTimeSeriesConverter() - Constructor for class org.openimaj.ml.timeseries.converter.DoubleProviderTimeSeriesConverter

DoubleSynchronisedTimeSeriesCollection - Class in org.openimaj.ml.timeseries.series

DoubleSynchronisedTimeSeriesCollection() - Constructor for class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

basic constructor

DoubleSynchronisedTimeSeriesCollection(IndependentPair<String, DoubleTimeSeries>...) - Constructor for class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

create a synchronised series from a bunch of pairs

DoubleTimeSeries - Class in org.openimaj.ml.timeseries.series

DoubleTimeSeries(int) - Constructor for class org.openimaj.ml.timeseries.series.DoubleTimeSeries

Convenience constructor, makes a time series with empty data of a given size

DoubleTimeSeries() - Constructor for class org.openimaj.ml.timeseries.series.DoubleTimeSeries

Sets the times and data arrays backing this class 0 length

DoubleTimeSeries(long[], double[]) - Constructor for class org.openimaj.ml.timeseries.series.DoubleTimeSeries

doubleTimeSeries() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

doubleTimeSeries() - Method in interface org.openimaj.ml.timeseries.series.DoubleTimeSeriesProvider

DoubleTimeSeriesCollection - Class in org.openimaj.ml.timeseries.series

A set of time DoubleTimeSeries which may not be synchronised.

DoubleTimeSeriesCollection() - Constructor for class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

DoubleTimeSeriesProvider - Interface in org.openimaj.ml.timeseries.series

Allows a given time series to express itself as a DoubleTimeSeries

E

equals(Object) - Method in class org.openimaj.ml.regression.LinearRegression

error(DoubleTimeSeries...) - Static method in class org.openimaj.ml.timeseries.aggregator.MeanSquaredDifferenceAggregator

errors - Variable in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

estimate(List<? extends IndependentPair<double[], Integer>>) - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

estimate(List<? extends IndependentPair<double[], double[]>>) - Method in class org.openimaj.ml.regression.LinearRegression

estimate(double[][], double[][]) - Method in class org.openimaj.ml.regression.LinearRegression

As in LinearRegression.estimate(List) but using double arrays for efficiency.

estimate(Matrix, Matrix) - Method in class org.openimaj.ml.regression.LinearRegression

As in LinearRegression.estimate(List) but using double arrays for efficiency.

estimate(Corpus) - Method in class org.openimaj.pgm.vb.lda.mle.LDALearner

initiates the EM algorithm on documents in the corpus

ETA0_BIAS - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The weighting of the subgradient of BIAS, weighted down each ETASTEPS number of iterations of the biconvex scheme, defaults to eta0 (0.05)

ETA0_U - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The weighting of the subgradient of U, weighted down each ETASTEPS number of iterations of the biconvex scheme, defaults to 0.05

eta0_u - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

eta0_u - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

ETA0_W - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The weighting of the subgradient of W, weighted down each ETASTEPS number of iterations of the biconvex scheme, defaults to 0.05

eta0_w - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

eta0_w - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

ETA_GAMMA - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The ammount by which ETA is made to increase each iteration

ETASTEPS - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The steps at which point the eta parameter is reduced, defaults to 3

etat(int, double) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatSquareLossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.SquareLossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.SquareMissingLossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

eval(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatSquareLossFunction

evaluate(double[], int, int, double) - Method in class org.openimaj.ml.kernel.HomogeneousKernelMap

Evaluate the kernel for the given x value.

evaluate(DoubleFV) - Method in class org.openimaj.ml.kernel.HomogeneousKernelMap

Compute the Homogeneous Kernel Map approximation of the given feature vector

evaluate(List<Pair<Matrix>>) - Method in class org.openimaj.ml.linear.evaluation.BilinearEvaluator

evaluate(List<Pair<Matrix>>) - Method in class org.openimaj.ml.linear.evaluation.MeanSumLossEvaluator

evaluate(List<Pair<Matrix>>) - Method in class org.openimaj.ml.linear.evaluation.RootMeanSumLossEvaluator

evaluate(List<Pair<Matrix>>) - Method in class org.openimaj.ml.linear.evaluation.SumLossEvaluator

EXPANDEDUINITSTRAT - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The initialisation strategy for U when it is expanded.

EXPANDEDWINITSTRAT - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The initialisation strategy for W when it is expanded.

expandY(Matrix) - Static method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

Given a flat value matrix, makes a diagonal sparse matrix containing the values as the diagonal

expandY(Matrix) - Static method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

Given a flat value matrix, makes a diagonal sparse matrix containing the values as the diagonal

extractFeature(T) - Method in class org.openimaj.ml.kernel.HomogeneousKernelMap.ExtractorWrapper

extractFeatures(ANNOTATION, FeatureExtractor<FEATURE, OBJECT>) - Method in class org.openimaj.ml.annotation.utils.AnnotatedListHelper

Extract the features corresponding to a specific annotation.

extractFeaturesExclude(ANNOTATION, FeatureExtractor<FEATURE, OBJECT>) - Method in class org.openimaj.ml.annotation.utils.AnnotatedListHelper

Extract the features corresponding to everything EXCEPT the specific given annotation.

extractor - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

ExtractorWrapper(FeatureExtractor<? extends FeatureVector, T>, HomogeneousKernelMap) - Constructor for class org.openimaj.ml.kernel.HomogeneousKernelMap.ExtractorWrapper

Construct with the given internal extractor and homogeneous kernel map.

F

FeatureCachingIncrementalBatchAnnotator<OBJECT,ANNOTATION,FEATURE> - Class in org.openimaj.ml.annotation

Adaptor that allows a BatchAnnotator to behave like a IncrementalAnnotator by caching extracted features and then performing training only when FeatureCachingIncrementalBatchAnnotator.annotate(Object) is called.

FeatureCachingIncrementalBatchAnnotator(FeatureExtractor<FEATURE, OBJECT>, BatchAnnotator<FEATURE, ANNOTATION>) - Constructor for class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

Construct with the given feature extractor and batch annotator, and use an in-memory cache.

FeatureCachingIncrementalBatchAnnotator(FeatureExtractor<FEATURE, OBJECT>, BatchAnnotator<FEATURE, ANNOTATION>, GroupedListCache<ANNOTATION, FEATURE>) - Constructor for class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

Construct with the given feature extractor and batch annotator, and use an in-memory cache.

features - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

FeatureVectorPCA - Class in org.openimaj.ml.pca

Principal Components Analysis wrapper for FeatureVectors.

FeatureVectorPCA() - Constructor for class org.openimaj.ml.pca.FeatureVectorPCA

Default constructor, using an SvdPrincipalComponentAnalysis.

FeatureVectorPCA(PrincipalComponentAnalysis) - Constructor for class org.openimaj.ml.pca.FeatureVectorPCA

Construct with the given PrincipalComponentAnalysis object.

FirstValueInitStrat - Class in org.openimaj.ml.linear.learner.init

Completely ignores desired dimensions and returns the first Y value seen

FirstValueInitStrat() - Constructor for class org.openimaj.ml.linear.learner.init.FirstValueInitStrat

FixedChooser - Class in org.openimaj.ml.annotation.basic.util

Always choose the same (fixed) number of annotations

FixedChooser(int) - Constructor for class org.openimaj.ml.annotation.basic.util.FixedChooser

Construct with the given number of annotations.

FixedDataGenerator<X,Y> - Class in org.openimaj.ml.linear.data

FixedDataGenerator(List<IndependentPair<X, Y>>) - Constructor for class org.openimaj.ml.linear.data.FixedDataGenerator

flatten() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

In some way flatten the held time series such that the output is:

flatten() - Method in class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

Fold(int[], int[], int[]) - Constructor for class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator.Fold

FOLD_ROOT(int) - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

FORCE_SPARCITY - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

Should all parameter matricies be held SparseMatrix instances and therefore remain sparse.

fromSign(double) - Static method in enum org.openimaj.ml.linear.learner.perceptron.PerceptronClass

G

GaussianTimeSeriesProcessor - Class in org.openimaj.ml.timeseries.processor

Calculates a moving average over a specified window in the past such that data[t_n] = sum^{m}_{i=1}{data[t_{n-i}} This processor returns a value for each time in the underlying time series.

GaussianTimeSeriesProcessor(double) - Constructor for class org.openimaj.ml.timeseries.processor.GaussianTimeSeriesProcessor

generate() - Method in class org.openimaj.ml.linear.data.BiconvexDataGenerator

generate() - Method in class org.openimaj.ml.linear.data.BiconvexIncrementalDataGenerator

generate() - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

generate() - Method in interface org.openimaj.ml.linear.data.DataGenerator

generate() - Method in class org.openimaj.ml.linear.data.FixedDataGenerator

generate() - Method in class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

generate() - Method in class org.openimaj.ml.linear.data.MatlabFileDataGenerator

generate() - Method in interface org.openimaj.ml.linear.data.MatrixDataGenerator

generate(DoubleFV) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

Generate a new "observation" as a linear combination of the principal components (PC): mean + PC * scaling.

generateAll() - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

get(ANNOTATION) - Method in class org.openimaj.ml.annotation.utils.AnnotatedListHelper

Retrieve all the items from the data that have a specific annotation.

get(long, int, int) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

get(long, long) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

get(long, int, int, TIMESERIES) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

get(long, long, long) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

get(long, int, int) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

get(long, int, int, TS) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

get(long, long, long) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

get(long, long) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

get(long, int, int) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

get(long, int, int, DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

get(long, long, long) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

get(long, long) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

get(long) - Method in class org.openimaj.ml.timeseries.TimeSeries

Same as calling TimeSeries.get(long, int, int) with spans as 0

get(long, int, int) - Method in class org.openimaj.ml.timeseries.TimeSeries

returns the DATA at a specific point in time and those before and after to the number requested.

get(long, int, int, RETURNTYPE) - Method in class org.openimaj.ml.timeseries.TimeSeries

Same as TimeSeries.get(long, int, int) but instead of createing the output DATA instance, an existing data instance is handed which is filled.

get(long, long, long) - Method in class org.openimaj.ml.timeseries.TimeSeries

returns the RETURNTYPE at a specific point in time and those before and after within the specified thresholds.

get(long, long) - Method in class org.openimaj.ml.timeseries.TimeSeries

returns the RETURNTYPE between the specified time periods.

getAnnotations() - Method in interface org.openimaj.ml.annotation.Annotated

getAnnotations() - Method in class org.openimaj.ml.annotation.AnnotatedObject

getAnnotations() - Method in interface org.openimaj.ml.annotation.Annotator

getAnnotations() - Method in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

getAnnotations(VectorNaiveBayesCategorizer<ANNOTATION, NaiveBayesAnnotator.PDF>, Vector) - Method in enum org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator.Mode

getAnnotations() - Method in class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.linear.LiblinearAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.linear.LinearSVMAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.model.ModelAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

getAnnotations() - Method in class org.openimaj.ml.annotation.utils.AnnotatedListHelper

Get the set of all known annotations

getBias() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

getBias() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

getBias() - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

getBias() - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

getBias() - Method in class org.openimaj.ml.linear.learner.perceptron.Projectron

getBias() - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Get the bias, b

getBilinearLearner(int, int) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

Construct a learner with the desired number of users and words.

getBilinearLearner() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

getConfig(LDALearner.LDAConfig) - Method in class org.openimaj.pgm.vb.lda.mle.LDALearner

getCurrentValues() - Method in class org.openimaj.ml.linear.learner.init.CurrentUMean

getCurrentValues() - Method in class org.openimaj.ml.linear.learner.init.CurrentValueMean

getCurrentValues() - Method in class org.openimaj.ml.linear.learner.init.CurrentWMean

getData() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

getData() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

getData() - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

getData() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

getData() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

getData() - Method in class org.openimaj.ml.timeseries.TimeSeries

getDependantValues() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

getDirection(List<double[]>, List<Double>) - Static method in class org.openimaj.ml.linear.kernel.LinearVectorKernel

getDocuments() - Method in class org.openimaj.pgm.util.Corpus

getErrors() - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

getExperimentName() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

getExperimentName() - Method in class org.openimaj.ml.linear.experiments.sinabill.StreamAustrianDampeningExperiments

getExperimentSetName() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

getK() - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

getMean() - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron

getMean() - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

getNames() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

getNormDirection() - Method in class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

getObject() - Method in interface org.openimaj.ml.annotation.Annotated

getObject() - Method in class org.openimaj.ml.annotation.AnnotatedObject

getOrigin() - Method in class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

getParams() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

getParams() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

getParams() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

getPlane() - Method in class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

getPlaneDirections(List<double[]>, List<Double>) - Static method in class org.openimaj.ml.linear.kernel.LinearVectorKernel

getPlanePoint(List<double[]>, List<Double>, double, double...) - Static method in class org.openimaj.ml.linear.kernel.LinearVectorKernel

On the plane

getReg() - Method in class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

getRegression() - Method in class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

getRegression() - Method in class org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

getRegression() - Method in class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

getSmoothSpectrum(double, HomogeneousKernelMap) - Method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.WindowType

getSpectrum(double) - Method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.KernelType

getSupports() - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

getSupports() - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

getSupports() - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron

getSupports() - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

getTasks() - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

getTime(long, long, long) - Static method in class org.openimaj.ml.timeseries.processor.interpolation.util.TimeSpanUtils

Get

getTime(long, long, int) - Static method in class org.openimaj.ml.timeseries.processor.interpolation.util.TimeSpanUtils

getTime(long, int, long) - Static method in class org.openimaj.ml.timeseries.processor.interpolation.util.TimeSpanUtils

getTimes() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

getTimes() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

getTimes() - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

getTimes() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

getTimes() - Method in class org.openimaj.ml.timeseries.TimeSeries

getTransform() - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Get the transform matrix W

getTyped(String) - Method in class org.openimaj.ml.linear.learner.LearningParameters

getU() - Method in class org.openimaj.ml.linear.data.BiconvexDataGenerator

getU() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

getU() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

getUsers() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

getVocabulary() - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

getVocabulary() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

getW() - Method in class org.openimaj.ml.linear.data.BiconvexDataGenerator

getW() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

getW() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

getWeights() - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

getWeights() - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

getWeights() - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatSquareLossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.SquareLossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.SquareMissingLossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

gradient(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatSquareLossFunction

H

HardCodedInitStrat - Class in org.openimaj.ml.linear.learner.init

Completely ignores desired dimensions and returns what it wants

HardCodedInitStrat() - Constructor for class org.openimaj.ml.linear.learner.init.HardCodedInitStrat

holdreg(boolean) - Method in class org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

holdreg(boolean) - Method in class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

HomogeneousKernelMap - Class in org.openimaj.ml.kernel

Implementation of the Homogeneous Kernel Map.

HomogeneousKernelMap(HomogeneousKernelMap.KernelType, HomogeneousKernelMap.WindowType) - Constructor for class org.openimaj.ml.kernel.HomogeneousKernelMap

Construct with the given kernel and window.

HomogeneousKernelMap(HomogeneousKernelMap.KernelType, double, HomogeneousKernelMap.WindowType) - Constructor for class org.openimaj.ml.kernel.HomogeneousKernelMap

Construct with the given kernel, gamma and window.

HomogeneousKernelMap(HomogeneousKernelMap.KernelType, double, int, HomogeneousKernelMap.WindowType) - Constructor for class org.openimaj.ml.kernel.HomogeneousKernelMap

Construct with the given kernel, gamma, order and window.

HomogeneousKernelMap(HomogeneousKernelMap.KernelType, double, int, double, HomogeneousKernelMap.WindowType) - Constructor for class org.openimaj.ml.kernel.HomogeneousKernelMap

Construct with the given kernel, gamma, order, period and window.

HomogeneousKernelMap.ExtractorWrapper<T> - Class in org.openimaj.ml.kernel

Helper implementation of a FeatureExtractor that wraps another FeatureExtractor and then applies the HomogeneousKernelMap to the output before returning the vector.

HomogeneousKernelMap.KernelType - Enum in org.openimaj.ml.kernel

Types of supported kernel for the HomogeneousKernelMap

HomogeneousKernelMap.WindowType - Enum in org.openimaj.ml.kernel

Types of window supported by the HomogeneousKernelMap.

I

IncompatibleTimeSeriesException - Exception in org.openimaj.ml.timeseries

IncompatibleTimeSeriesException() - Constructor for exception org.openimaj.ml.timeseries.IncompatibleTimeSeriesException

IncompatibleTimeSeriesException(String) - Constructor for exception org.openimaj.ml.timeseries.IncompatibleTimeSeriesException

IncrementalAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation

An Annotator that can be trained/updated incrementally.

IncrementalAnnotator() - Constructor for class org.openimaj.ml.annotation.IncrementalAnnotator

IncrementalBilinearSparseOnlineLearner - Class in org.openimaj.ml.linear.learner

IncrementalBilinearSparseOnlineLearner() - Constructor for class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

Instantiates with the default params

IncrementalBilinearSparseOnlineLearner(BilinearLearnerParameters) - Constructor for class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

IncrementalTrainer<T> - Interface in org.openimaj.ml.training

Interface describing objects capable of performing incremental training.

incTopicTotal(int, double) - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

Increment a topic and word index by d.

incTopicWord(int, int, double) - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

Increment a topic and word index by d.

IndependentPriorRandomAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.basic

Annotator that randomly assigns annotations, but takes account of the prior probability of each annotation based on the proportion of times it occurred in training.

IndependentPriorRandomAnnotator(NumAnnotationsChooser) - Constructor for class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

Construct with the given NumAnnotationsChooser to determine how many annotations are produced by calls to IndependentPriorRandomAnnotator.annotate(Object).

indexes(BillMatlabFileDataGenerator.Fold) - Method in enum org.openimaj.ml.linear.data.BillMatlabFileDataGenerator.Mode

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.CurrentValueMean

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.FirstValueInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.HardCodedInitStrat

init(int, int) - Method in interface org.openimaj.ml.linear.learner.init.InitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.MatlabFileInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.RandomInitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SingleValueInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseOnesInitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseRandomInitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseRowOnesInitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseRowRandomInitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseSingleValueInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.init.SparseZerosInitStrategy

init(int, int) - Method in interface org.openimaj.ml.linear.learner.matlib.init.InitStrategy

init(int, int) - Method in class org.openimaj.ml.linear.learner.matlib.init.SingleValueInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.matlib.init.SparseSingleValueInitStrat

init(int, int) - Method in class org.openimaj.ml.linear.learner.matlib.init.SparseZerosInitStrategy

initialise(double[][], double[][], boolean[]) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Initialise the LMDR with the given data in three parallel data arrays.

initModel(LDAModel, Corpus) - Method in interface org.openimaj.pgm.vb.lda.mle.LDABetaInitStrategy

Given a model and the corpus initialise the model's sufficient statistics

initModel(LDAModel, Corpus) - Method in class org.openimaj.pgm.vb.lda.mle.LDABetaInitStrategy.RandomBetaInit

InitStrategy - Interface in org.openimaj.ml.linear.learner.init

Initialise a matrix to some dimension

InitStrategy - Interface in org.openimaj.ml.linear.learner.matlib.init

Initialise a matrix to some dimension

InstanceCachingIncrementalBatchAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation

Adaptor that allows a BatchAnnotator to behave like a IncrementalAnnotator by caching instances and then performing training only when InstanceCachingIncrementalBatchAnnotator.annotate(Object) is called.

InstanceCachingIncrementalBatchAnnotator(BatchAnnotator<OBJECT, ANNOTATION>) - Constructor for class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

Construct with an in-memory cache and the given batch annotator.

InstanceCachingIncrementalBatchAnnotator(BatchAnnotator<OBJECT, ANNOTATION>, GroupedListCache<ANNOTATION, OBJECT>) - Constructor for class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

Construct with the given batch annotator and cache implementation.

integerRange(double[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.ArrayIndexComparator

internalAssign(TIMESERIES) - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

internalAssign(Collection<Long>, Collection<DATA>) - Method in interface org.openimaj.ml.timeseries.collection.TimeSeriesCollectionAssignable

Assign these values of data and time to the internal collection

internalAssign(TS) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

internalAssign(long[], DATA[]) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

internalAssign(Collection<Long>, Collection<DATA>) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

internalAssign(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

internalAssign(Collection<Long>, Collection<Double>) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

internalAssign(long[], double[]) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

internalAssign(DoubleTimeSeriesCollection) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

internalAssign(RETURNTYPE) - Method in class org.openimaj.ml.timeseries.TimeSeries

internalAssign(long[], DATA) - Method in class org.openimaj.ml.timeseries.TimeSeries

internalNewInstance() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

internalNewInstance() - Method in class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

internalNewInstance() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

interpolate(DoubleTimeSeries, long[]) - Method in class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

interpolate(DoubleTimeSeries, long, long, long) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

Uses TimeSeriesInterpolation.interpolate(DoubleTimeSeries, long[]) to return an interpolation of the construction TimeSeries between the times at the required interval

interpolate(DoubleTimeSeries, long, int, long) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

Uses TimeSeriesInterpolation.interpolate(DoubleTimeSeries, long[]) to return an interpolation of the construction TimeSeries from begin, for a number of steps with a given delta between steps

interpolate(DoubleTimeSeries, long, long, int) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

Uses TimeSeriesInterpolation.interpolate(DoubleTimeSeries,long[]) to return an interpolation of the construction TimeSeries from begin, until end with a delta which means that there are splits time instances

interpolate(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

interpolate(DoubleTimeSeries, long[]) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

IntervalSummationProcessor<ALLDATA,DATA,TS extends TimeSeries<ALLDATA,DATA,TS> & TimeSeriesArithmaticOperator<DATA,TS> & TimeSeriesCollectionAssignable<DATA,TS>> - Class in org.openimaj.ml.timeseries.processor

Given time step calculate each timestep such that value[timeStep(x)] = sum from x-1 to x as n [ timeStep(n) ] The exact meaning of "sum" for any given timestep data must be defined.

IntervalSummationProcessor(long[]) - Constructor for class org.openimaj.ml.timeseries.processor.IntervalSummationProcessor

A processor which maps across given time steps

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.loss.MatSquareLossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.loss.SquareLossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.loss.SquareMissingLossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

isMatrixLoss() - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatSquareLossFunction

iterator() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearLearnerParametersLineSearch

iterator() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

iterator() - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

iterator() - Method in class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

iterator() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

K

k - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

k - Variable in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

Kernel<T> - Interface in org.openimaj.ml.linear.kernel

A function which takes in two T instances and returns a double

KernelPerceptron<INDEPENDANT,DEPENDANT> - Class in org.openimaj.ml.linear.learner.perceptron

KernelPerceptron() - Constructor for class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

KernelPerceptron(Kernel<INDEPENDANT>) - Constructor for class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

KNNAnnotator<OBJECT,ANNOTATION,FEATURE> - Class in org.openimaj.ml.annotation.basic

Annotator based on a multi-class k-nearest-neighbour classifier.

KNNAnnotator(FeatureExtractor<FEATURE, OBJECT>, DistanceComparator<? super FEATURE>, float) - Constructor for class org.openimaj.ml.annotation.basic.KNNAnnotator

Construct with the given extractor, comparator and threshold.

KNNAnnotator(FeatureExtractor<FEATURE, OBJECT>, DistanceComparator<? super FEATURE>) - Constructor for class org.openimaj.ml.annotation.basic.KNNAnnotator

Construct with the given extractor and comparator.

KNNAnnotator(FeatureExtractor<FEATURE, OBJECT>, DistanceComparator<? super FEATURE>, int) - Constructor for class org.openimaj.ml.annotation.basic.KNNAnnotator

Construct with the given extractor, comparator and number of neighbours.

KNNAnnotator(FeatureExtractor<FEATURE, OBJECT>, DistanceComparator<? super FEATURE>, int, float) - Constructor for class org.openimaj.ml.annotation.basic.KNNAnnotator

Construct with the given extractor, comparator, number of neighbours and threshold.

L

L1L2Regulariser - Class in org.openimaj.ml.linear.learner.matlib.regul

L1L2Regulariser() - Constructor for class org.openimaj.ml.linear.learner.matlib.regul.L1L2Regulariser

L1L2Regulariser - Class in org.openimaj.ml.linear.learner.regul

L1L2Regulariser() - Constructor for class org.openimaj.ml.linear.learner.regul.L1L2Regulariser

L1Regulariser - Class in org.openimaj.ml.linear.learner.matlib.regul

L1Regulariser() - Constructor for class org.openimaj.ml.linear.learner.matlib.regul.L1Regulariser

L1Regulariser - Class in org.openimaj.ml.linear.learner.regul

L1Regulariser() - Constructor for class org.openimaj.ml.linear.learner.regul.L1Regulariser

LAMBDA - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The parameter of the regulariser for both W and U

LAMBDA_U - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The parameter of the regulariser for U, defaults to LAMBDA

lambda_u - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

lambda_u - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

LAMBDA_W - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The parameter of the regulariser for W, defaults to LAMBDA

lambda_w - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

lambda_w - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

LambdaSearchAustrian - Class in org.openimaj.ml.linear.experiments.sinabill

Optimise lambda and eta0 and learning rates with a line search

LambdaSearchAustrian() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

LargeMarginDimensionalityReduction - Class in org.openimaj.ml.linear.projection

LargeMarginDimensionalityReduction is a technique to compress high dimensional features into a lower-dimension representation using a learned linear projection.

LargeMarginDimensionalityReduction(int) - Constructor for class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Construct with the given target dimensionality and default values for the other parameters (learning rate of 1.0 for W; learning rate of 0 for bias).

LargeMarginDimensionalityReduction(int, double, double) - Constructor for class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Construct with the given target dimensionality learning rates.

LDABetaInitStrategy - Interface in org.openimaj.pgm.vb.lda.mle

Initialisation strategies for the beta matrix in the maximum liklihood LDA.

LDABetaInitStrategy.RandomBetaInit - Class in org.openimaj.pgm.vb.lda.mle

initialises beta randomly s.t.

LDALearner - Class in org.openimaj.pgm.vb.lda.mle

An implementation of variational inference LDA which can be saved and loaded

LDALearner(int) - Constructor for class org.openimaj.pgm.vb.lda.mle.LDALearner

LDAModel - Class in org.openimaj.pgm.vb.lda.mle

Holds the sufficient statistics for a maximum liklihood LDA as well as a single value for alpha (the parameter of the topic dirichlet prior)

LDAModel(int) - Constructor for class org.openimaj.pgm.vb.lda.mle.LDAModel

LDAVariationlState - Class in org.openimaj.pgm.vb.lda.mle

The state of the E step of the MLE LDA

LDAVariationlState(LDAModel) - Constructor for class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

The variational state holds phi and gamma states as well as information for convergence of the E step.

learnBasis(FeatureVector[]) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

Learn the PCA basis of the given feature vectors.

learnBasis(Collection<? extends FeatureVector>) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

Learn the PCA basis of the given feature vectors.

learnBasis(double[][]) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

learnBasisNorm(Matrix) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

learner - Variable in class org.openimaj.ml.linear.evaluation.BilinearEvaluator

learner - Variable in class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

LearningParameters - Class in org.openimaj.ml.linear.learner

LearningParameters() - Constructor for class org.openimaj.ml.linear.learner.LearningParameters

LearningParameters(Map<String, Object>) - Constructor for class org.openimaj.ml.linear.learner.LearningParameters

LearningParameters.Placeholder - Class in org.openimaj.ml.linear.learner

length() - Method in class org.openimaj.pgm.util.Document

LiblinearAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.linear

Annotator based on linear classifiers learned using Liblinear (see Linear) or DenseLinear depending on the density of the features.

LiblinearAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>, LiblinearAnnotator.Mode, SolverType, double, double) - Constructor for class org.openimaj.ml.annotation.linear.LiblinearAnnotator

Default constructor.

LiblinearAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>, LiblinearAnnotator.Mode, SolverType, double, double, double, boolean) - Constructor for class org.openimaj.ml.annotation.linear.LiblinearAnnotator

Default constructor.

LiblinearAnnotator.Mode - Enum in org.openimaj.ml.annotation.linear

The classifier mode; either multiclass or multilabel.

LiblinearHelper - Class in org.openimaj.ml.annotation.utils

Helper methods for interoperability of OpenIMAJ types with Liblinear.

LiblinearHelper() - Constructor for class org.openimaj.ml.annotation.utils.LiblinearHelper

likelihood - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

The liklihood of the current topic sufficient statistics and variational parameters

LinearInterpolationProcessor - Class in org.openimaj.ml.timeseries.processor.interpolation

Perform a linear interpolation such that the value of data at time t1 between t0 and t2 = data[t1] = data[t0] * (t1 - t0)/(t2-t0) + data[t2] * (t2 - t1)/(t2-t0) Note that this means if data is known at t1, then t0 = t1 and data[t1] == data[t0]

LinearInterpolationProcessor(long[]) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

LinearInterpolationProcessor() - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

LinearInterpolationProcessor(long, int, long) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

LinearInterpolationProcessor(long, long, int) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

LinearInterpolationProcessor(long, long, long) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor

LinearPerceptronDataGenerator - Class in org.openimaj.ml.linear.data

LinearPerceptronDataGenerator(double, int, double) - Constructor for class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

LinearPerceptronDataGenerator(double, int, double, int) - Constructor for class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

The range for each dimension

LinearRegression - Class in org.openimaj.ml.regression

Given a set of independant variables a linear regressions finds the optimal vector B such that: (Y - Xb)^2 = 0 (Y - Xb)^{T}(Y-Xb) = 0 calculated by assuming a convex shape of (Y - Xb) with varying values of b (reasonable as the function is linear) and then calculating the point at which the first derivative of this function is 0.

LinearRegression() - Constructor for class org.openimaj.ml.regression.LinearRegression

linear regression model

LinearRegressionProcessor - Class in org.openimaj.ml.timeseries.processor

Using a LinearRegression model, a time series is used as input to calculate the coefficients of a linear regression such that value = b * time + c This is the simplest kind of model that can be applied to a time series

LinearRegressionProcessor() - Constructor for class org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

Calculate the regression from the same time series inputed

LinearRegressionProcessor(LinearRegression) - Constructor for class org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

Use reg as the linear regression to predict.

LinearSVMAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.linear

An Annotator based on a set of linear SVMs (one per annotation).

LinearSVMAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>, ANNOTATION) - Constructor for class org.openimaj.ml.annotation.linear.LinearSVMAnnotator

Construct a new LinearSVMAnnotator with the given extractor and the specified negative class.

LinearSVMAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>) - Constructor for class org.openimaj.ml.annotation.linear.LinearSVMAnnotator

Construct a new LinearSVMAnnotator with the given extractor.

LinearVectorKernel - Class in org.openimaj.ml.linear.kernel

LinearVectorKernel() - Constructor for class org.openimaj.ml.linear.kernel.LinearVectorKernel

loadModel(File) - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Load an existing svm model.

LOSS - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The loss function, defaults to SquareMissingLossFunction

loss - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

loss - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

LossFunction - Class in org.openimaj.ml.linear.learner.loss

With a held Y and X, return gradient and evaluations of a loss function of some parameters s.t.

LossFunction() - Constructor for class org.openimaj.ml.linear.learner.loss.LossFunction

LossFunction - Class in org.openimaj.ml.linear.learner.matlib.loss

With a held Y and X, return gradient and evaluations of a loss function of some parameters s.t.

LossFunction() - Constructor for class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

M

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.AustrianWordExperiments

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.BilinearLearnerParametersLineSearch

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianDampeningExperiments

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperiments

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperimentsNormalised

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.BillWordInvestigation

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.RegretExperiment

main(String[]) - Static method in class org.openimaj.ml.linear.experiments.sinabill.StreamAustrianDampeningExperiments

makeKernel(double, double) - Static method in class org.openimaj.ml.timeseries.processor.GaussianTimeSeriesProcessor

Construct a zero-mean Gaussian with the specified standard deviation.

mapping(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

MarginMeanCenteredPerceptron - Class in org.openimaj.ml.linear.learner.perceptron

MarginMeanCenteredPerceptron(VectorKernel, double) - Constructor for class org.openimaj.ml.linear.learner.perceptron.MarginMeanCenteredPerceptron

MarginMeanCenteredPerceptron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.MarginMeanCenteredPerceptron

MATLAB_DATA() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

MATLAB_DATA(String) - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

MatlabFileDataGenerator - Class in org.openimaj.ml.linear.data

MatlabFileDataGenerator(File) - Constructor for class org.openimaj.ml.linear.data.MatlabFileDataGenerator

MatlabFileInitStrat - Class in org.openimaj.ml.linear.learner.init

Given a matlab file, return its matrix held in the "arr" field as the initialisation matrix

MatlabFileInitStrat(File) - Constructor for class org.openimaj.ml.linear.learner.init.MatlabFileInitStrat

MatlibBilinearSparseOnlineLearner - Class in org.openimaj.ml.linear.learner.matlib

An implementation of a stochastic gradient decent with proximal perameter adjustment (for regularised parameters).

MatlibBilinearSparseOnlineLearner() - Constructor for class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

The default parameters.

MatlibBilinearSparseOnlineLearner(BilinearLearnerParameters) - Constructor for class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

MatLossFunction - Class in org.openimaj.ml.linear.learner.loss

MatLossFunction(LossFunction) - Constructor for class org.openimaj.ml.linear.learner.loss.MatLossFunction

MatLossFunction - Class in org.openimaj.ml.linear.learner.matlib.loss

MatLossFunction(LossFunction) - Constructor for class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

MatrixDataGenerator<T extends gov.sandia.cognition.math.matrix.Matrix> - Interface in org.openimaj.ml.linear.data

Generates instances of some system of the form: Y = f(X)

MatSquareLossFunction - Class in org.openimaj.ml.linear.learner.loss

MatSquareLossFunction() - Constructor for class org.openimaj.ml.linear.learner.loss.MatSquareLossFunction

MatSquareLossFunction - Class in org.openimaj.ml.linear.learner.matlib.loss

MatSquareLossFunction() - Constructor for class org.openimaj.ml.linear.learner.matlib.loss.MatSquareLossFunction

max - Variable in class org.openimaj.ml.annotation.basic.util.RandomChooser

MeanCenteredKernelPerceptron - Class in org.openimaj.ml.linear.learner.perceptron

MeanCenteredKernelPerceptron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron

MeanCenteredProjectron - Class in org.openimaj.ml.linear.learner.perceptron

MeanCenteredProjectron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

MeanCenteredProjectron(VectorKernel, double) - Constructor for class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

MeanSquaredDifferenceAggregator - Class in org.openimaj.ml.timeseries.aggregator

MeanSquaredDifferenceAggregator() - Constructor for class org.openimaj.ml.timeseries.aggregator.MeanSquaredDifferenceAggregator

MeanSumLossEvaluator - Class in org.openimaj.ml.linear.evaluation

MeanSumLossEvaluator() - Constructor for class org.openimaj.ml.linear.evaluation.MeanSumLossEvaluator

min - Variable in class org.openimaj.ml.annotation.basic.util.RandomChooser

ModelAnnotator<OBJECT,ANNOTATION,FEATURE> - Class in org.openimaj.ml.annotation.model

An BatchAnnotator backed by a EstimatableModel.

ModelAnnotator(FeatureExtractor<FEATURE, OBJECT>, EstimatableModel<FEATURE, ANNOTATION>, Set<ANNOTATION>) - Constructor for class org.openimaj.ml.annotation.model.ModelAnnotator

Construct with the given parameters.

MovingAverageProcessor - Class in org.openimaj.ml.timeseries.processor

Calculates a moving average over a specified window in the past such that data[t_n] = sum^{m}_{i=1}{data[t_{n-i}} This processor returns a value for each time in the underlying time series.

MovingAverageProcessor(long) - Constructor for class org.openimaj.ml.timeseries.processor.MovingAverageProcessor

N

NaiveBayesAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.bayes

Annotator based on a Naive Bayes Classifier.

NaiveBayesAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>, NaiveBayesAnnotator.Mode) - Constructor for class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

Construct a NaiveBayesAnnotator with the given feature extractor and mode of operation.

NaiveBayesAnnotator.Mode - Enum in org.openimaj.ml.annotation.bayes

Modes of operation for prediction using the NaiveBayesAnnotator.

name - Variable in class org.openimaj.ml.linear.learner.LearningParameters.Placeholder

ndims - Variable in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

NEGATIVE_CLASS - Static variable in class org.openimaj.ml.annotation.svm.SVMAnnotator

The input to the SVM model for negative classes

newClassificationEvaluator(ClassificationAnalyser<RESULT, ANNOTATION, OBJECT>) - Method in class org.openimaj.ml.annotation.evaluation.AnnotationEvaluator

Make a new ClassificationEvaluator, backed by the annotations computed by this AnnotationEvaluator, with the given ClassificationAnalyser.

newInstance(Collection<Long>, Collection<DATA>) - Method in interface org.openimaj.ml.timeseries.collection.TimeSeriesCollectionAssignable

newInstance(Collection<Long>, Collection<DATA>) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

newInstance() - Method in class org.openimaj.ml.timeseries.series.DoubleSynchronisedTimeSeriesCollection

newInstance() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

newInstance(Collection<Long>, Collection<Double>) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

newInstance() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

newInstance() - Method in class org.openimaj.ml.timeseries.TimeSeries

newInstance() - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

This method also swaps the likelihoods (i.e.

newRetrievalEvaluator(RetrievalAnalyser<RESULT, ANNOTATION, OBJECT>) - Method in class org.openimaj.ml.annotation.evaluation.AnnotationEvaluator

Make a new RetrievalEvaluator, backed by the annotations computed by this AnnotationEvaluator, with the given RetrievalAnalyser.

nFolds() - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

nn - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

nSeries() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

ntopics - Variable in class org.openimaj.pgm.vb.lda.mle.LDAModel

number of topics

numAnnotations - Variable in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

numAnnotations - Variable in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

numAnnotations - Variable in class org.openimaj.ml.annotation.basic.util.FixedChooser

numAnnotations() - Method in class org.openimaj.ml.annotation.basic.util.FixedChooser

numAnnotations() - Method in interface org.openimaj.ml.annotation.basic.util.NumAnnotationsChooser

numAnnotations() - Method in class org.openimaj.ml.annotation.basic.util.PriorChooser

numAnnotations() - Method in class org.openimaj.ml.annotation.basic.util.RandomChooser

NumAnnotationsChooser - Interface in org.openimaj.ml.annotation.basic.util

Choose how many annotations to produce.

numItemsToEstimate() - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

numItemsToEstimate() - Method in class org.openimaj.ml.regression.LinearRegression

O

object - Variable in class org.openimaj.ml.annotation.AnnotatedObject

The annotated object

OISVM - Class in org.openimaj.ml.linear.learner.perceptron

OISVM(Kernel<double[]>, double) - Constructor for class org.openimaj.ml.linear.learner.perceptron.OISVM

oldLikelihood - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

The old liklihood

oldphi - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

Useful for calculating the sumphi for a given document

OnesInitStrategy - Class in org.openimaj.ml.linear.learner.init

OnesInitStrategy() - Constructor for class org.openimaj.ml.linear.learner.init.OnesInitStrategy

OnesZeroRowInitStrategy - Class in org.openimaj.ml.linear.learner.init

OnesZeroRowInitStrategy() - Constructor for class org.openimaj.ml.linear.learner.init.OnesZeroRowInitStrategy

OnlineLearner<INDEPENDANT,DEPENDANT> - Interface in org.openimaj.ml.linear.learner

org.openimaj.math.matrix - package org.openimaj.math.matrix

org.openimaj.ml.annotation - package org.openimaj.ml.annotation

org.openimaj.ml.annotation.basic - package org.openimaj.ml.annotation.basic

org.openimaj.ml.annotation.basic.util - package org.openimaj.ml.annotation.basic.util

org.openimaj.ml.annotation.bayes - package org.openimaj.ml.annotation.bayes

org.openimaj.ml.annotation.evaluation - package org.openimaj.ml.annotation.evaluation

org.openimaj.ml.annotation.linear - package org.openimaj.ml.annotation.linear

org.openimaj.ml.annotation.model - package org.openimaj.ml.annotation.model

org.openimaj.ml.annotation.svm - package org.openimaj.ml.annotation.svm

org.openimaj.ml.annotation.utils - package org.openimaj.ml.annotation.utils

org.openimaj.ml.dataset - package org.openimaj.ml.dataset

org.openimaj.ml.kernel - package org.openimaj.ml.kernel

org.openimaj.ml.linear.data - package org.openimaj.ml.linear.data

org.openimaj.ml.linear.evaluation - package org.openimaj.ml.linear.evaluation

org.openimaj.ml.linear.experiments.sinabill - package org.openimaj.ml.linear.experiments.sinabill

org.openimaj.ml.linear.kernel - package org.openimaj.ml.linear.kernel

org.openimaj.ml.linear.learner - package org.openimaj.ml.linear.learner

org.openimaj.ml.linear.learner.init - package org.openimaj.ml.linear.learner.init

org.openimaj.ml.linear.learner.loss - package org.openimaj.ml.linear.learner.loss

org.openimaj.ml.linear.learner.matlib - package org.openimaj.ml.linear.learner.matlib

org.openimaj.ml.linear.learner.matlib.init - package org.openimaj.ml.linear.learner.matlib.init

org.openimaj.ml.linear.learner.matlib.loss - package org.openimaj.ml.linear.learner.matlib.loss

org.openimaj.ml.linear.learner.matlib.regul - package org.openimaj.ml.linear.learner.matlib.regul

org.openimaj.ml.linear.learner.perceptron - package org.openimaj.ml.linear.learner.perceptron

org.openimaj.ml.linear.learner.regul - package org.openimaj.ml.linear.learner.regul

org.openimaj.ml.linear.projection - package org.openimaj.ml.linear.projection

org.openimaj.ml.pca - package org.openimaj.ml.pca

org.openimaj.ml.regression - package org.openimaj.ml.regression

org.openimaj.ml.timeseries - package org.openimaj.ml.timeseries

org.openimaj.ml.timeseries.aggregator - package org.openimaj.ml.timeseries.aggregator

org.openimaj.ml.timeseries.collection - package org.openimaj.ml.timeseries.collection

org.openimaj.ml.timeseries.converter - package org.openimaj.ml.timeseries.converter

org.openimaj.ml.timeseries.processor - package org.openimaj.ml.timeseries.processor

org.openimaj.ml.timeseries.processor.interpolation - package org.openimaj.ml.timeseries.processor.interpolation

org.openimaj.ml.timeseries.processor.interpolation.util - package org.openimaj.ml.timeseries.processor.interpolation.util

org.openimaj.ml.timeseries.series - package org.openimaj.ml.timeseries.series

org.openimaj.ml.training - package org.openimaj.ml.training

org.openimaj.pgm.util - package org.openimaj.pgm.util

org.openimaj.pgm.vb.lda.mle - package org.openimaj.pgm.vb.lda.mle

P

params - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

params - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

PerceptronClass - Enum in org.openimaj.ml.linear.learner.perceptron

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.AustrianWordExperiments

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianDampeningExperiments

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperiments

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.BillAustrianExperimentsNormalised

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.BillWordInvestigation

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.RegretExperiment

performExperiment() - Method in class org.openimaj.ml.linear.experiments.sinabill.StreamAustrianDampeningExperiments

phi - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

the n'th unique word in a document's probability for each topic

Placeholder(String) - Constructor for class org.openimaj.ml.linear.learner.LearningParameters.Placeholder

PlusOneDoubleArrayKernelPerceptron - Class in org.openimaj.ml.linear.learner.perceptron

An implementation of a simple KernelPerceptron which works with double array inputs and is binary.

PlusOneDoubleArrayKernelPerceptron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.PlusOneDoubleArrayKernelPerceptron

POSITIVE_CLASS - Static variable in class org.openimaj.ml.annotation.svm.SVMAnnotator

The input to the SVM model for positive classes

predict(Matrix) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

predict(Map<String, Map<String, Double>>) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

predict(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

predict(INDEPENDANT) - Method in interface org.openimaj.ml.linear.learner.OnlineLearner

predict(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

predict(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.OISVM

predict(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

predict(double[]) - Method in class org.openimaj.ml.linear.learner.perceptron.ThresholdDoubleArrayKernelPerceptron

predict(double[]) - Method in class org.openimaj.ml.regression.LinearRegression

predict(Matrix) - Method in class org.openimaj.ml.regression.LinearRegression

Helper function which adds the constant component to x and returns predicted values for y, one per row

prepare(Corpus) - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

initialises the sufficient statistic holder based on ntopics and the Corpus.vocabularySize().

prepare(int) - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

initialises the sufficient statistic holder based on ntopics and the vocabularySize.

prepare(Document) - Method in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

initialises the phi and sets everything to 0

prepareExperimentLog(BilinearLearnerParameters) - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

prepareExperimentLog() - Method in class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

prepareExperimentRoot() - Method in class org.openimaj.ml.linear.experiments.sinabill.BilinearExperiment

prepareExperimentRoot() - Method in class org.openimaj.ml.linear.experiments.sinabill.LambdaSearchAustrian

PriorChooser - Class in org.openimaj.ml.annotation.basic.util

Choose the number of annotations based on the numbers of annotations of each training example.

PriorChooser() - Constructor for class org.openimaj.ml.annotation.basic.util.PriorChooser

process(Matrix, Matrix) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

process(Map<String, Map<String, Double>>, Map<String, Double>) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

process(Matrix, Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

process(INDEPENDANT, DEPENDANT) - Method in interface org.openimaj.ml.linear.learner.OnlineLearner

process(INDEPENDANT, DEPENDANT) - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

process(double[], PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.MarginMeanCenteredPerceptron

process(double[], PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.OISVM

process(double[], Integer) - Method in class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

process(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.GaussianTimeSeriesProcessor

process(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

process(TS) - Method in class org.openimaj.ml.timeseries.processor.IntervalSummationProcessor

process(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

process(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.MovingAverageProcessor

process(TIMESERIES) - Method in interface org.openimaj.ml.timeseries.processor.TimeSeriesProcessor

process(DoubleTimeSeries) - Method in class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

process(TimeSeriesProcessor<DATA, SINGLE_TYPE, RETURNTYPE>) - Method in class org.openimaj.ml.timeseries.TimeSeries

process using the provided processor, return

processInplace(TimeSeriesProcessor<DATA, SINGLE_TYPE, RETURNTYPE>) - Method in class org.openimaj.ml.timeseries.TimeSeries

Process using the provided processor

processInternal(TimeSeriesProcessor<ALLINPUT, SINGLEINPUT, INTERNALSERIES>) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

process the internal series held by this collection with this processor

processInternalInplace(TimeSeriesProcessor<ALLINPUT, SINGLEINPUT, INTERNALSERIES>) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

process the internal series held by this collection with this processor

project(double[]) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Compute the low rank estimate of the given vector

project(FeatureVector) - Method in class org.openimaj.ml.pca.FeatureVectorPCA

Project a vector by the basis.

Projectron - Class in org.openimaj.ml.linear.learner.perceptron

Projectron(VectorKernel, double) - Constructor for class org.openimaj.ml.linear.learner.perceptron.Projectron

Projectron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.Projectron

prox(Matrix, double) - Method in class org.openimaj.ml.linear.learner.matlib.regul.L1L2Regulariser

prox(Matrix, double) - Method in class org.openimaj.ml.linear.learner.matlib.regul.L1Regulariser

prox(Matrix, double) - Method in interface org.openimaj.ml.linear.learner.matlib.regul.Regulariser

prox(Matrix, double) - Method in class org.openimaj.ml.linear.learner.regul.L1L2Regulariser

prox(Matrix, double) - Method in class org.openimaj.ml.linear.learner.regul.L1Regulariser

prox(Matrix, double) - Method in interface org.openimaj.ml.linear.learner.regul.Regulariser

R

RandomBetaInit() - Constructor for class org.openimaj.pgm.vb.lda.mle.LDABetaInitStrategy.RandomBetaInit

unseeded random

RandomBetaInit(int) - Constructor for class org.openimaj.pgm.vb.lda.mle.LDABetaInitStrategy.RandomBetaInit

seeded random

RandomChooser - Class in org.openimaj.ml.annotation.basic.util

Choose a random number of annotations between the given limits.

RandomChooser(int) - Constructor for class org.openimaj.ml.annotation.basic.util.RandomChooser

Construct so that the maximum possible number of annotations is max and the minimum is 0.

RandomChooser(int, int) - Constructor for class org.openimaj.ml.annotation.basic.util.RandomChooser

Construct so that the minimium possible number of annotations is min and the maximum is max.

RandomInitStrategy - Class in org.openimaj.ml.linear.learner.init

RandomInitStrategy(double, double, Random) - Constructor for class org.openimaj.ml.linear.learner.init.RandomInitStrategy

readASCII(Scanner) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

readASCII(Scanner) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

readBinary(DataInput) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

readBinary(DataInput) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

readCorpus() - Method in interface org.openimaj.pgm.util.CorpusReader

readCorpus() - Method in class org.openimaj.pgm.util.SimpleCorpusReader

recomputeBias(double[][], double[][], boolean[]) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

RegretExperiment - Class in org.openimaj.ml.linear.experiments.sinabill

RegretExperiment() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.RegretExperiment

REGUL - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The regularisation function, defaults to L1L2Regulariser

regul - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

regul - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

Regulariser - Interface in org.openimaj.ml.linear.learner.matlib.regul

Regulariser - Interface in org.openimaj.ml.linear.learner.regul

reinitParams() - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

must be called if any parameters are changed

reinitParams() - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

reinitParams() - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

must be called if any parameters are changed

reset() - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

reset() - Method in class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

reset() - Method in class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

reset() - Method in class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

reset() - Method in interface org.openimaj.ml.training.IncrementalTrainer

Reset the object to its initial condition, as if it hasn't seen any training data.

RestrictedAnnotator<OBJECT,ANNOTATION> - Interface in org.openimaj.ml.annotation

The RestrictedAnnotator interface describes annotators that can predict annotations based on an external context that restricts what annotations are allowed.

rnd - Variable in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

rng - Variable in class org.openimaj.ml.annotation.basic.util.RandomChooser

RootMeanSumLossEvaluator - Class in org.openimaj.ml.linear.evaluation

RootMeanSumLossEvaluator() - Constructor for class org.openimaj.ml.linear.evaluation.RootMeanSumLossEvaluator

S

score(double[], double[]) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Compute the matching score between a pair of (high dimensional) features.

ScoredAnnotation<ANNOTATION> - Class in org.openimaj.ml.annotation

An annotation that was produced automatically with a given confidence.

ScoredAnnotation(ANNOTATION, float) - Constructor for class org.openimaj.ml.annotation.ScoredAnnotation

Construct with the given annotation and confidence

SEED - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The random seed of any randomised components of this learner (usually initialisation).

series(String) - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

series(String) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

set(long[], Map<String, ALLINPUT>) - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

set(long[], Map<String, ALLINPUT>) - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

set(long[], DATA[]) - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

set(long[], double[]) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

set(long[], DATA) - Method in class org.openimaj.ml.timeseries.TimeSeries

Set the data associated with each time.

setAlpha(double) - Method in class org.openimaj.pgm.vb.lda.mle.LDAModel

setAnswerElement(int, int, double) - Method in class org.openimaj.math.matrix.ThreadedMatrixMulti

setBias(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

setBias(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

setBias(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

setBias(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

setBias(double) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

setContext(INDEPENDANT, DEPENDANT) - Method in class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

setContext(INDEPENDANT, DEPENDANT) - Method in interface org.openimaj.ml.linear.learner.init.ContextAwareInitStrategy

The current INDEPENDANT and DEPENDANT values at the time of initialization

setError(double) - Method in class org.openimaj.ml.linear.data.LinearPerceptronDataGenerator

setFold(int, BillMatlabFileDataGenerator.Mode) - Method in class org.openimaj.ml.linear.data.BillMatlabFileDataGenerator

setK(int) - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

Set the number of neighbours

setLearner(BilinearSparseOnlineLearner) - Method in class org.openimaj.ml.linear.evaluation.BilinearEvaluator

setLearner(OnlineLearner<INDEPENDANT, DEPENDANT>) - Method in class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

setLearner(OnlineLearner<INDEPENDANT, DEPENDANT>) - Method in interface org.openimaj.ml.linear.learner.init.ContextAwareInitStrategy

The

setMatrix(Matrix) - Method in class org.openimaj.ml.linear.learner.init.HardCodedInitStrat

The matrix to init things to

setSaveModel(File) - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Set whether to save the SVM model to disk.

setTransform(Matrix) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

setU(Matrix) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

setU(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

setW(Matrix) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

setW(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

setWordCount(int, int) - Method in class org.openimaj.pgm.util.Document

sets a word in the document's count.

setX(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

setX(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

setX(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

setX(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

setY(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

setY(Matrix) - Method in class org.openimaj.ml.linear.learner.loss.MatLossFunction

setY(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

setY(Matrix) - Method in class org.openimaj.ml.linear.learner.matlib.loss.MatLossFunction

SimpleCorpusReader - Class in org.openimaj.pgm.util

A corpus from a document whose lines are documents and whose words are seperated by a space

SimpleCorpusReader(InputStream) - Constructor for class org.openimaj.pgm.util.SimpleCorpusReader

SimplePerceptron - Class in org.openimaj.ml.linear.learner.perceptron

SimplePerceptron() - Constructor for class org.openimaj.ml.linear.learner.perceptron.SimplePerceptron

SingleValueInitStrat - Class in org.openimaj.ml.linear.learner.init

SingleValueInitStrat(double) - Constructor for class org.openimaj.ml.linear.learner.init.SingleValueInitStrat

SingleValueInitStrat - Class in org.openimaj.ml.linear.learner.matlib.init

SingleValueInitStrat(double) - Constructor for class org.openimaj.ml.linear.learner.matlib.init.SingleValueInitStrat

size() - Method in class org.openimaj.ml.linear.data.MatlabFileDataGenerator

size() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

size() - Method in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

size() - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

size() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

size() - Method in class org.openimaj.ml.timeseries.TimeSeries

size() - Method in class org.openimaj.pgm.util.Corpus

smf - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

SparseOnesInitStrategy - Class in org.openimaj.ml.linear.learner.init

SparseOnesInitStrategy(double, Random) - Constructor for class org.openimaj.ml.linear.learner.init.SparseOnesInitStrategy

SparseRandomInitStrategy - Class in org.openimaj.ml.linear.learner.init

SparseRandomInitStrategy(double, double, double, Random) - Constructor for class org.openimaj.ml.linear.learner.init.SparseRandomInitStrategy

SparseRowOnesInitStrategy - Class in org.openimaj.ml.linear.learner.init

SparseRowOnesInitStrategy(double, Random) - Constructor for class org.openimaj.ml.linear.learner.init.SparseRowOnesInitStrategy

SparseRowRandomInitStrategy - Class in org.openimaj.ml.linear.learner.init

SparseRowRandomInitStrategy(double, double, double, Random) - Constructor for class org.openimaj.ml.linear.learner.init.SparseRowRandomInitStrategy

SparseSingleValueInitStrat - Class in org.openimaj.ml.linear.learner.init

SparseSingleValueInitStrat(double) - Constructor for class org.openimaj.ml.linear.learner.init.SparseSingleValueInitStrat

SparseSingleValueInitStrat - Class in org.openimaj.ml.linear.learner.matlib.init

SparseSingleValueInitStrat(double) - Constructor for class org.openimaj.ml.linear.learner.matlib.init.SparseSingleValueInitStrat

SparseZerosInitStrategy - Class in org.openimaj.ml.linear.learner.init

SparseZerosInitStrategy() - Constructor for class org.openimaj.ml.linear.learner.init.SparseZerosInitStrategy

SparseZerosInitStrategy - Class in org.openimaj.ml.linear.learner.matlib.init

SparseZerosInitStrategy() - Constructor for class org.openimaj.ml.linear.learner.matlib.init.SparseZerosInitStrategy

SquaredSummedDifferenceAggregator - Class in org.openimaj.ml.timeseries.aggregator

SquaredSummedDifferenceAggregator() - Constructor for class org.openimaj.ml.timeseries.aggregator.SquaredSummedDifferenceAggregator

SquareLossFunction - Class in org.openimaj.ml.linear.learner.loss

SquareLossFunction() - Constructor for class org.openimaj.ml.linear.learner.loss.SquareLossFunction

SquareMissingLossFunction - Class in org.openimaj.ml.linear.learner.loss

SquareMissingLossFunction() - Constructor for class org.openimaj.ml.linear.learner.loss.SquareMissingLossFunction

state - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

The current LDAModel (i.e.

step(double[], double[], boolean) - Method in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

Perform a single update step of the SGD optimisation.

StreamAustrianDampeningExperiments - Class in org.openimaj.ml.linear.experiments.sinabill

StreamAustrianDampeningExperiments() - Constructor for class org.openimaj.ml.linear.experiments.sinabill.StreamAustrianDampeningExperiments

sum() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

sum() - Method in interface org.openimaj.ml.timeseries.TimeSeriesArithmaticOperator

add together all time series data elements

sumLoss(List<Pair<Matrix>>, Matrix, Matrix, Matrix, BilinearLearnerParameters) - Method in class org.openimaj.ml.linear.evaluation.MeanSumLossEvaluator

sumLoss(List<Pair<Matrix>>, Matrix, Matrix, Matrix, BilinearLearnerParameters) - Method in class org.openimaj.ml.linear.evaluation.RootMeanSumLossEvaluator

sumLoss(List<Pair<Matrix>>, Matrix, Matrix, Matrix, BilinearLearnerParameters) - Method in class org.openimaj.ml.linear.evaluation.SumLossEvaluator

SumLossEvaluator - Class in org.openimaj.ml.linear.evaluation

SumLossEvaluator() - Constructor for class org.openimaj.ml.linear.evaluation.SumLossEvaluator

supportIndex - Variable in class org.openimaj.ml.linear.learner.perceptron.OISVM

supports - Variable in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

supports - Variable in class org.openimaj.ml.linear.learner.perceptron.OISVM

SVMAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.svm

Wraps the libsvm SVM and provides basic positive/negative annotation for a single class.

SVMAnnotator(FeatureExtractor<? extends FeatureVector, OBJECT>) - Constructor for class org.openimaj.ml.annotation.svm.SVMAnnotator

Constructor that takes the feature extractor to use.

SynchronisedTimeSeriesCollection<ALLINPUT,SINGLEINPUT,TIMESERIES extends SynchronisedTimeSeriesCollection<ALLINPUT,SINGLEINPUT,TIMESERIES,INTERNALSERIES>,INTERNALSERIES extends TimeSeries<ALLINPUT,SINGLEINPUT,INTERNALSERIES>> - Class in org.openimaj.ml.timeseries.collection

A collection of time series which share exactly the same time steps.

SynchronisedTimeSeriesCollection() - Constructor for class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

initialise the underlying time series holder

SynchronisedTimeSeriesCollectionAggregator<TIMESERIES extends TimeSeries<?,?,TIMESERIES>,STSCOLLECTION extends SynchronisedTimeSeriesCollection<?,?,?,TIMESERIES>,OUTPUT> - Interface in org.openimaj.ml.timeseries.aggregator

A time series collection aggregators take as input a time series collection and output a specified type

T

terms - Variable in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

test_backtrack(Matrix, Matrix, Matrix, double) - Method in class org.openimaj.ml.linear.learner.loss.LossFunction

test_backtrack(Matrix, Matrix, Matrix, double) - Method in class org.openimaj.ml.linear.learner.loss.MatSquareLossFunction

ThreadedMatrixMulti - Class in org.openimaj.math.matrix

Perform a multithreaded matrix multiplication

ThreadedMatrixMulti() - Constructor for class org.openimaj.math.matrix.ThreadedMatrixMulti

ThreadedMatrixMulti(int, int) - Constructor for class org.openimaj.math.matrix.ThreadedMatrixMulti

threshold - Variable in class org.openimaj.ml.annotation.basic.KNNAnnotator

ThresholdDoubleArrayKernelPerceptron - Class in org.openimaj.ml.linear.learner.perceptron

An implementation of a simple KernelPerceptron which works with double array inputs and is binary.

ThresholdDoubleArrayKernelPerceptron(VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.ThresholdDoubleArrayKernelPerceptron

ThresholdDoubleArrayKernelPerceptron(double, double, VectorKernel) - Constructor for class org.openimaj.ml.linear.learner.perceptron.ThresholdDoubleArrayKernelPerceptron

times(Matrix, Matrix) - Method in class org.openimaj.math.matrix.ThreadedMatrixMulti

times(double[][], double[][]) - Method in class org.openimaj.math.matrix.ThreadedMatrixMulti

TimeSeries<DATA,SINGLE_TYPE,RETURNTYPE extends TimeSeries<DATA,SINGLE_TYPE,RETURNTYPE>> - Class in org.openimaj.ml.timeseries

A time series defines data at discrete points in time.

TimeSeries() - Constructor for class org.openimaj.ml.timeseries.TimeSeries

TimeSeriesArithmaticOperator<DATA,TS extends TimeSeries<?,DATA,TS>> - Interface in org.openimaj.ml.timeseries

An object which defines a set of arithmatic operations of the represented time series

TimeSeriesCollection<ALLINPUT,SINGLEINPUT,TIMESERIES extends TimeSeriesCollection<ALLINPUT,SINGLEINPUT,TIMESERIES,INTERNALSERIES>,INTERNALSERIES extends TimeSeries<ALLINPUT,SINGLEINPUT,INTERNALSERIES>> - Class in org.openimaj.ml.timeseries.collection

A collection of time series which share exactly the same time steps.

TimeSeriesCollection() - Constructor for class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

initialise the underlying time series holder

TimeSeriesCollectionAggregator<TIMESERIES extends TimeSeries<?,?,TIMESERIES>,TSCOLLECTION extends TimeSeriesCollection<?,?,?,TIMESERIES>,OUTPUT> - Interface in org.openimaj.ml.timeseries.aggregator

A time series collection aggregators take as input a time series collection and output a specified type

TimeSeriesCollectionAssignable<DATA,TS extends TimeSeries<?,DATA,TS>> - Interface in org.openimaj.ml.timeseries.collection

An object which can initialise a time series based on two java collections for time and data

TimeSeriesConverter<INPUTALL,INPUTSINGLE,INPUTTS extends TimeSeries<INPUTALL,INPUTSINGLE,INPUTTS>,OUTPUTALL,OUTPUTSINGLE,OUTPUTTS extends TimeSeries<OUTPUTALL,OUTPUTSINGLE,OUTPUTTS>> - Interface in org.openimaj.ml.timeseries.converter

timeSeriesHolder - Variable in class org.openimaj.ml.timeseries.collection.TimeSeriesCollection

TimeSeriesInterpolation - Class in org.openimaj.ml.timeseries.processor.interpolation

Interpolate values of a time series.

TimeSeriesInterpolation() - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

The processor's times are set to default, i.e.

TimeSeriesInterpolation(long, long, long) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

TimeSeriesInterpolation(long, int, long) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

TimeSeriesInterpolation(long, long, int) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

TimeSeriesInterpolation(long[]) - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation

TimeSeriesProcessor<ALLDATA,SINGLEDATA,TIMESERIES extends TimeSeries<ALLDATA,SINGLEDATA,TIMESERIES>> - Interface in org.openimaj.ml.timeseries.processor

A time series processor alters a type of TimeSeries in place.

TimeSeriesSetException - Exception in org.openimaj.ml.timeseries

Thrown if time series are set with insufficient times/dates

TimeSeriesSetException(String) - Constructor for exception org.openimaj.ml.timeseries.TimeSeriesSetException

Constructs a new TimeSeriesSetException with the given message.

TimeSeriesSetException() - Constructor for exception org.openimaj.ml.timeseries.TimeSeriesSetException

Constructs a new TimeSeriesSetException with the default message.

TimeSpanUtils - Class in org.openimaj.ml.timeseries.processor.interpolation.util

Some utility functions used by various TimeSeries classes to get arrays of spans of time

TimeSpanUtils() - Constructor for class org.openimaj.ml.timeseries.processor.interpolation.util.TimeSpanUtils

topicTotal - Variable in class org.openimaj.pgm.vb.lda.mle.LDAModel

The maximum likelihood sufficient statistics for estimation of Beta.

topicWord - Variable in class org.openimaj.pgm.vb.lda.mle.LDAModel

The maximum likelihood sufficient statistics for estimation of Beta.

toString() - Method in class org.openimaj.ml.annotation.ScoredAnnotation

toString() - Method in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

toString() - Method in class org.openimaj.ml.regression.LinearRegression

toString() - Method in class org.openimaj.ml.timeseries.collection.SynchronisedTimeSeriesCollection

toString() - Method in class org.openimaj.ml.timeseries.series.ConcreteTimeSeries

toString() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

toString() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

toString() - Method in class org.openimaj.ml.timeseries.TimeSeries

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.basic.IndependentPriorRandomAnnotator

train(Annotated<OBJECT, ANNOTATION>) - Method in class org.openimaj.ml.annotation.basic.KNNAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

train(List<? extends Annotated<O, A>>) - Method in class org.openimaj.ml.annotation.basic.util.FixedChooser

train(List<? extends Annotated<O, A>>) - Method in interface org.openimaj.ml.annotation.basic.util.NumAnnotationsChooser

Train the chooser with the given data.

train(List<? extends Annotated<O, A>>) - Method in class org.openimaj.ml.annotation.basic.util.PriorChooser

train(List<? extends Annotated<O, A>>) - Method in class org.openimaj.ml.annotation.basic.util.RandomChooser

train(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT>) - Method in class org.openimaj.ml.annotation.BatchAnnotator

Train the annotator with the given grouped dataset.

train(Annotated<OBJECT, ANNOTATION>) - Method in class org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator

train(Annotated<OBJECT, ANNOTATION>) - Method in class org.openimaj.ml.annotation.FeatureCachingIncrementalBatchAnnotator

train(Iterable<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.IncrementalAnnotator

Train the annotator with the given data.

train(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT>) - Method in class org.openimaj.ml.annotation.IncrementalAnnotator

Train the annotator with the given grouped dataset.

train(Annotated<OBJECT, ANNOTATION>) - Method in class org.openimaj.ml.annotation.InstanceCachingIncrementalBatchAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.linear.LiblinearAnnotator

train(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT>) - Method in class org.openimaj.ml.annotation.linear.LiblinearAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.linear.LinearSVMAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.model.ModelAnnotator

train(List<? extends Annotated<OBJECT, ANNOTATION>>) - Method in class org.openimaj.ml.annotation.svm.SVMAnnotator

Train the object with the given data.

train(List<? extends T>) - Method in interface org.openimaj.ml.training.BatchTrainer

Train the object with the given data.

train(Iterable<? extends T>) - Method in interface org.openimaj.ml.training.IncrementalTrainer

Train the object with the given data.

train(T) - Method in interface org.openimaj.ml.training.IncrementalTrainer

Train/update object using a new instance.

trainMultiClass(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT>) - Method in class org.openimaj.ml.annotation.IncrementalAnnotator

Train the annotator with the given grouped dataset.

transform - Variable in class org.openimaj.ml.annotation.linear.DenseLinearTransformAnnotator

U

u - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

u - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

UINITSTRAT - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The initialisation strategy for U.

UniformRandomAnnotator<OBJECT,ANNOTATION> - Class in org.openimaj.ml.annotation.basic

An annotator that chooses annotations completely randomly from the set of all known annotations.

UniformRandomAnnotator(NumAnnotationsChooser) - Constructor for class org.openimaj.ml.annotation.basic.UniformRandomAnnotator

Construct with the given NumAnnotationsChooser to determine how many annotations are produced by calls to UniformRandomAnnotator.annotate(Object).

update(double[], PerceptronClass, PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

update(INDEPENDANT, DEPENDANT, DEPENDANT) - Method in class org.openimaj.ml.linear.learner.perceptron.KernelPerceptron

When there is an error in prediction, update somehow

update(double[], PerceptronClass, PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron

update(double[], PerceptronClass, PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.MeanCenteredProjectron

update(double[], PerceptronClass, PerceptronClass) - Method in class org.openimaj.ml.linear.learner.perceptron.Projectron

updateBias(Matrix, Matrix, Matrix, double) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

updateBias(Matrix, double) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

updateU(Matrix, Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

updateU(Matrix, Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.BilinearUnmixedSparseOnlineLearner

updateU(Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

updateUserValues(Map<String, Map<String, Double>>, Map<String, Double>) - Method in class org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner

Update the incremental learner and underlying weight matricies to reflect potentially novel users, words and values to learn against

updateW(Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

updateW(Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.BilinearUnmixedSparseOnlineLearner

updateW(Matrix, double, double) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

V

v() - Method in enum org.openimaj.ml.linear.learner.perceptron.PerceptronClass

valueOf(String) - Static method in enum org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator.Mode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.openimaj.ml.annotation.linear.LiblinearAnnotator.Mode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.KernelType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.WindowType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.openimaj.ml.linear.data.BillMatlabFileDataGenerator.Mode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.openimaj.ml.linear.learner.perceptron.PerceptronClass

Returns the enum constant of this type with the specified name.

values() - Static method in enum org.openimaj.ml.annotation.bayes.NaiveBayesAnnotator.Mode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.openimaj.ml.annotation.linear.LiblinearAnnotator.Mode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.KernelType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.openimaj.ml.kernel.HomogeneousKernelMap.WindowType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.openimaj.ml.linear.data.BillMatlabFileDataGenerator.Mode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.openimaj.ml.linear.learner.perceptron.PerceptronClass

Returns an array containing the constants of this enum type, in the order they are declared.

varGamma - Variable in class org.openimaj.pgm.vb.lda.mle.LDAVariationlState

the dirichlet parameter for the topic multinomials

VectorKernel - Interface in org.openimaj.ml.linear.kernel

vocabularySize() - Method in class org.openimaj.pgm.util.Corpus

W

w - Variable in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

w - Variable in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

W - Variable in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

weights - Variable in class org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron

WindowedLinearRegressionAggregator - Class in org.openimaj.ml.timeseries.aggregator

An implementation of a general linear regressive such that the values of a timeseries Y are predicted using the values of a set of time series X at some offset over some time window.

WindowedLinearRegressionAggregator(String) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Calculate the regression from the same time series inputed

WindowedLinearRegressionAggregator(String, boolean) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Calculate the regression from the same time series inputed

WindowedLinearRegressionAggregator(String, int) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionAggregator(String, int, boolean) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionAggregator(String, int, int) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionAggregator(String, int, int, boolean) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionAggregator(String, int, int, boolean, DoubleSynchronisedTimeSeriesCollection) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionAggregator(String, boolean, IndependentPair<Integer, Integer>...) - Constructor for class org.openimaj.ml.timeseries.aggregator.WindowedLinearRegressionAggregator

Perform regression s.t.

WindowedLinearRegressionProcessor - Class in org.openimaj.ml.timeseries.processor

An implementation of an autoregressive model such that Xt = b*X{t-offset-window,t-offset} + c

WindowedLinearRegressionProcessor() - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

Calculate the regression from the same time series inputed

WindowedLinearRegressionProcessor(int) - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

Perform regression s.t.

WindowedLinearRegressionProcessor(int, int) - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

Perform regression s.t.

WindowedLinearRegressionProcessor(LinearRegression) - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

Use reg as the linear regression to predict.

WindowedLinearRegressionProcessor(DoubleTimeSeries, int) - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

WindowedLinearRegressionProcessor(DoubleTimeSeries, int, int) - Constructor for class org.openimaj.ml.timeseries.processor.WindowedLinearRegressionProcessor

WineDataset - Class in org.openimaj.ml.dataset

A Dataset instance of the standard wine clustering experiment found here:

WineDataset(Integer...) - Constructor for class org.openimaj.ml.dataset.WineDataset

Loads the wine dataset, mean centres the dataset

WineDataset(boolean, Integer...) - Constructor for class org.openimaj.ml.dataset.WineDataset

Loads the wine dataset from wine.data

WINITSTRAT - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

The initialisation strategy for W.

wLearnRate - Variable in class org.openimaj.ml.linear.projection.LargeMarginDimensionalityReduction

writeASCII(PrintWriter) - Method in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

writeASCII(PrintWriter) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

writeASCII(PrintWriter) - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeriesCollection

writeBinary(DataOutput) - Method in class org.openimaj.ml.linear.learner.BilinearSparseOnlineLearner

writeBinary(DataOutput) - Method in class org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

X

x - Variable in class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

X - Variable in class org.openimaj.ml.linear.learner.loss.LossFunction

X - Variable in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

Y

y - Variable in class org.openimaj.ml.linear.learner.init.AbstractContextAwareInitStrategy

Y - Variable in class org.openimaj.ml.linear.learner.loss.LossFunction

Y - Variable in class org.openimaj.ml.linear.learner.matlib.loss.LossFunction

Z

Z_STANDARDISE - Static variable in class org.openimaj.ml.linear.learner.BilinearLearnerParameters

Whether the Vprime and Dprime matrices should be zscore standardised

zero() - Method in class org.openimaj.ml.timeseries.series.DoubleTimeSeries

zero() - Method in interface org.openimaj.ml.timeseries.TimeSeriesArithmaticOperator

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