Uses of Class
org.apache.ignite.ml.structures.LabeledVector

Packages that use LabeledVector

Package	Description
org.apache.ignite.ml.composition.combinators.sequential	Contains sequential combinators of trainers and models.
org.apache.ignite.ml.dataset.feature.extractor	Package for upstream object vectorizations.
org.apache.ignite.ml.dataset.feature.extractor.impl	Package contains default implementations of Vectorizer.
org.apache.ignite.ml.dataset.impl.bootstrapping	Base package for bootstrapped implementation of machine learning dataset.
org.apache.ignite.ml.knn	Contains main APIs for kNN algorithms.
org.apache.ignite.ml.knn.ann	Contains main APIs for ANN classification algorithms.
org.apache.ignite.ml.knn.utils	Contains util functionality for kNN algorithms.
org.apache.ignite.ml.knn.utils.indices	Contains utils functionality for indices in kNN algorithms.
org.apache.ignite.ml.math.primitives.vector	Contains vector related classes.
org.apache.ignite.ml.preprocessing	Base package for machine learning preprocessing classes.
org.apache.ignite.ml.preprocessing.binarization	Contains binarization preprocessor.
org.apache.ignite.ml.preprocessing.developer	Contains Developer API preprocessors.
org.apache.ignite.ml.preprocessing.encoding.onehotencoder	Contains one hot encoding preprocessor.
org.apache.ignite.ml.preprocessing.encoding.stringencoder	Contains string encoding preprocessor.
org.apache.ignite.ml.preprocessing.imputing	Contains Imputer preprocessor.
org.apache.ignite.ml.preprocessing.maxabsscaling	Contains Max Abs Scaler preprocessor.
org.apache.ignite.ml.preprocessing.minmaxscaling	Contains Min Max Scaler preprocessor.
org.apache.ignite.ml.preprocessing.normalization	Contains Normalizer preprocessor.
org.apache.ignite.ml.preprocessing.standardscaling	Contains Standard scaler preprocessor.
org.apache.ignite.ml.structures	Contains some internal utility structures.
org.apache.ignite.ml.structures.partition	Contains internal APIs for dataset partitioned labeled datasets.
org.apache.ignite.ml.trainers	Contains model trainers.
org.apache.ignite.ml.util.generators	Contains utility classes for data streams generation.
org.apache.ignite.ml.util.generators.standard	Contains classes for predefined data stream generators.

Uses of LabeledVector in org.apache.ignite.ml.composition.combinators.sequential

Fields in org.apache.ignite.ml.composition.combinators.sequential with type parameters of type LabeledVector

Modifier and Type	Field and Description
protected IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>	TrainersSequentialComposition.datasetMapping Dataset mapping.
protected IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>	TrainersSequentialComposition.datasetMapping Dataset mapping.

Method parameters in org.apache.ignite.ml.composition.combinators.sequential with type arguments of type LabeledVector

Modifier and Type	Method and Description
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.

Constructor parameters in org.apache.ignite.ml.composition.combinators.sequential with type arguments of type LabeledVector

Constructor and Description
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.

Uses of LabeledVector in org.apache.ignite.ml.dataset.feature.extractor

Methods in org.apache.ignite.ml.dataset.feature.extractor that return LabeledVector

Modifier and Type	Method and Description
LabeledVector<L>	Vectorizer.apply(K key, V value) Extracts labeled vector from upstream object.
LabeledVector<L>	Vectorizer.extract(K k, V v) Extract LabeledVector from key and value.

Uses of LabeledVector in org.apache.ignite.ml.dataset.feature.extractor.impl

Methods in org.apache.ignite.ml.dataset.feature.extractor.impl with parameters of type LabeledVector

Modifier and Type	Method and Description
protected List<Integer>	LabeledDummyVectorizer.allCoords(K key, LabeledVector<L> value) Returns list of all coordinate with feature values.
protected Double	LabeledDummyVectorizer.feature(Integer coord, K key, LabeledVector<L> value) Extracts feature value by given coordinate.
protected L	LabeledDummyVectorizer.label(Integer coord, K key, LabeledVector<L> value) Extract label value by given coordinate.

Uses of LabeledVector in org.apache.ignite.ml.dataset.impl.bootstrapping

Subclasses of LabeledVector in org.apache.ignite.ml.dataset.impl.bootstrapping

Modifier and Type	Class and Description
class	BootstrappedVector Represents vector with repetitions counters for subsamples in bootstrapped dataset.

Uses of LabeledVector in org.apache.ignite.ml.knn

Methods in org.apache.ignite.ml.knn that return LabeledVector

Modifier and Type	Method and Description
protected @NotNull LabeledVector[]	NNClassificationModel.getKClosestVectors(LabeledVectorSet<LabeledVector> trainingData, TreeMap<Double,Set<Integer>> distanceIdxPairs) Iterates along entries in distance map and fill the resulting k-element array.

Methods in org.apache.ignite.ml.knn that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
protected LabeledVectorSet<LabeledVector>	NNClassificationModel.buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions)
List<LabeledVector<L>>	KNNModel.findKClosest(int k, Vector pnt) Finds k closest elements to the specified point.

Method parameters in org.apache.ignite.ml.knn with type arguments of type LabeledVector

Modifier and Type	Method and Description
protected LabeledVectorSet<LabeledVector>	NNClassificationModel.buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions)
protected @NotNull TreeMap<Double,Set<Integer>>	NNClassificationModel.getDistances(Vector v, LabeledVectorSet<LabeledVector<Double>> trainingData) Computes distances between given vector and each vector in training dataset.
protected @NotNull LabeledVector[]	NNClassificationModel.getKClosestVectors(LabeledVectorSet<LabeledVector> trainingData, TreeMap<Double,Set<Integer>> distanceIdxPairs) Iterates along entries in distance map and fill the resulting k-element array.

Uses of LabeledVector in org.apache.ignite.ml.knn.ann

Methods in org.apache.ignite.ml.knn.ann that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
LabeledVectorSet<LabeledVector>	ANNClassificationModel.getCandidates()
LabeledVectorSet<LabeledVector>	ANNModelFormat.getCandidates()

Constructor parameters in org.apache.ignite.ml.knn.ann with type arguments of type LabeledVector

Constructor and Description
ANNClassificationModel(LabeledVectorSet<LabeledVector> centers, ANNClassificationTrainer.CentroidStat centroindsStat) Build the model based on a candidates set.
ANNModelFormat(int k, DistanceMeasure distanceMeasure, boolean weighted, LabeledVectorSet<LabeledVector> candidates, ANNClassificationTrainer.CentroidStat candidatesStat) Creates an instance.

Uses of LabeledVector in org.apache.ignite.ml.knn.utils

Methods in org.apache.ignite.ml.knn.utils that return LabeledVector

Modifier and Type	Method and Description
LabeledVector<L>	PointWithDistance.getPnt()

Methods in org.apache.ignite.ml.knn.utils that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
static <K,V,C extends Serializable> Dataset<EmptyContext,LabeledVectorSet<LabeledVector>>	KNNUtils.buildDataset(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer) Builds dataset.
static <L> List<LabeledVector<L>>	PointWithDistanceUtil.transfomToListOrdered(Queue<PointWithDistance<L>> points) Util method that transforms collection of PointWithDistance to array of LabeledVector.

Methods in org.apache.ignite.ml.knn.utils with parameters of type LabeledVector

Modifier and Type	Method and Description
static <L> void	PointWithDistanceUtil.tryToAddIntoHeap(Queue<PointWithDistance<L>> heap, int k, LabeledVector<L> dataPnt, double distance) Util method that adds data point into heap if it fits (if heap size is less than k or a distance from taget point to data point is less than a distance from target point to the most distant data point in heap).

Method parameters in org.apache.ignite.ml.knn.utils with type arguments of type LabeledVector

Modifier and Type	Method and Description
static <L> void	PointWithDistanceUtil.tryToAddIntoHeap(Queue<PointWithDistance<L>> heap, int k, Vector pnt, List<LabeledVector<L>> dataPnts, DistanceMeasure distanceMeasure) Util method that adds data points into heap if they fits (if heap size is less than k or a distance from taget point to data point is less than a distance from target point to the most distant data point in heap).

Constructors in org.apache.ignite.ml.knn.utils with parameters of type LabeledVector

Constructor and Description
PointWithDistance(LabeledVector<L> pnt, double distance) Constructs a new instance of data point with distance.

Uses of LabeledVector in org.apache.ignite.ml.knn.utils.indices

Methods in org.apache.ignite.ml.knn.utils.indices that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
List<LabeledVector<L>>	KDTreeSpatialIndex.findKClosest(int k, Vector pnt) Finds k closest elements to the specified point.
List<LabeledVector<L>>	ArraySpatialIndex.findKClosest(int k, Vector pnt) Finds k closest elements to the specified point.
List<LabeledVector<L>>	SpatialIndex.findKClosest(int k, Vector pnt) Finds k closest elements to the specified point.
List<LabeledVector<L>>	BallTreeSpatialIndex.findKClosest(int k, Vector pnt) Finds k closest elements to the specified point.

Constructor parameters in org.apache.ignite.ml.knn.utils.indices with type arguments of type LabeledVector

Constructor and Description
ArraySpatialIndex(List<LabeledVector<L>> data, DistanceMeasure distanceMeasure) Construct a new array spatial index.
BallTreeSpatialIndex(List<LabeledVector<L>> data, DistanceMeasure distanceMeasure) Constructs a new instance of Ball tree spatial index.
KDTreeSpatialIndex(List<LabeledVector<L>> data, DistanceMeasure distanceMeasure) Constructs a new instance of KD tree spatial index.

Uses of LabeledVector in org.apache.ignite.ml.math.primitives.vector

Methods in org.apache.ignite.ml.math.primitives.vector that return LabeledVector

Modifier and Type	Method and Description
default <L> LabeledVector<L>	Vector.labeled(L lbl) Creates LabeledVector instance.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing

Method parameters in org.apache.ignite.ml.preprocessing with type arguments of type LabeledVector

Modifier and Type	Method and Description
default <L1,L2> Preprocessor<K,V>	Preprocessor.map(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> func) Map vectorizer answer.
default <L1,L2> Preprocessor<K,V>	Preprocessor.map(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> func) Map vectorizer answer.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.binarization

Methods in org.apache.ignite.ml.preprocessing.binarization that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	BinarizationPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.developer

Methods in org.apache.ignite.ml.preprocessing.developer that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	PatchedPreprocessor.apply(K k, V v) Applies this preprocessor.
LabeledVector<L1>	MappedPreprocessor.apply(K key, V value)

Constructor parameters in org.apache.ignite.ml.preprocessing.developer with type arguments of type LabeledVector

Constructor and Description
MappedPreprocessor(Preprocessor<K,V> original, IgniteFunction<LabeledVector<L0>,LabeledVector<L1>> andThen) Creates an instance of MappedPreprocessor.
MappedPreprocessor(Preprocessor<K,V> original, IgniteFunction<LabeledVector<L0>,LabeledVector<L1>> andThen) Creates an instance of MappedPreprocessor.
PatchedPreprocessor(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> lbPatcher, Preprocessor<K,V> basePreprocessor) Constructs a new instance of Binarization preprocessor.
PatchedPreprocessor(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> lbPatcher, Preprocessor<K,V> basePreprocessor) Constructs a new instance of Binarization preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.encoding.onehotencoder

Methods in org.apache.ignite.ml.preprocessing.encoding.onehotencoder that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	OneHotEncoderPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.encoding.stringencoder

Methods in org.apache.ignite.ml.preprocessing.encoding.stringencoder that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	StringEncoderPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.imputing

Methods in org.apache.ignite.ml.preprocessing.imputing that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	ImputerPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.maxabsscaling

Methods in org.apache.ignite.ml.preprocessing.maxabsscaling that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	MaxAbsScalerPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.minmaxscaling

Methods in org.apache.ignite.ml.preprocessing.minmaxscaling that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	MinMaxScalerPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.normalization

Methods in org.apache.ignite.ml.preprocessing.normalization that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	NormalizationPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.preprocessing.standardscaling

Methods in org.apache.ignite.ml.preprocessing.standardscaling that return LabeledVector

Modifier and Type	Method and Description
LabeledVector	StandardScalerPreprocessor.apply(K k, V v) Applies this preprocessor.

Uses of LabeledVector in org.apache.ignite.ml.structures

Classes in org.apache.ignite.ml.structures with type parameters of type LabeledVector

Modifier and Type	Class and Description
class	LabeledVectorSet<Row extends LabeledVector> The set of labeled vectors used in local partition calculations.

Constructors in org.apache.ignite.ml.structures with parameters of type LabeledVector

Constructor and Description
LabeledVectorSet(Row[] data) Creates new Labeled Dataset by given data.
LabeledVectorSet(Row[] data, int colSize) Creates new Labeled Dataset by given data.

Uses of LabeledVector in org.apache.ignite.ml.structures.partition

Methods in org.apache.ignite.ml.structures.partition that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
LabeledVectorSet<LabeledVector>	LabeledDatasetPartitionDataBuilderOnHeap.build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx) Builds a new partition data from a partition upstream data and partition context.

Uses of LabeledVector in org.apache.ignite.ml.trainers

Methods in org.apache.ignite.ml.trainers that return LabeledVector

Modifier and Type	Method and Description
LabeledVector<L>	FeatureLabelExtractor.extract(K k, V v) Extract LabeledVector from key and value.

Method parameters in org.apache.ignite.ml.trainers with type arguments of type LabeledVector

Modifier and Type	Method and Description
<O1,M1 extends IgniteModel<O,O1>> TrainersSequentialComposition<I,O,O1,L>	AdaptableDatasetTrainer.andThen(DatasetTrainer<M1,L> tr, IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMappingProducer) Create a TrainersSequentialComposition of whis trainer and specified trainer.
<O1,M1 extends IgniteModel<O,O1>> TrainersSequentialComposition<I,O,O1,L>	AdaptableDatasetTrainer.andThen(DatasetTrainer<M1,L> tr, IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMappingProducer) Create a TrainersSequentialComposition of whis trainer and specified trainer.

Uses of LabeledVector in org.apache.ignite.ml.util.generators

Methods in org.apache.ignite.ml.util.generators that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
Stream<LabeledVector<Double>>	DataStreamGenerator.labeled()
default Stream<LabeledVector<Double>>	DataStreamGenerator.labeled(IgniteFunction<Vector,Double> classifier)

Method parameters in org.apache.ignite.ml.util.generators with type arguments of type LabeledVector

Modifier and Type	Method and Description
default <K> void	DataStreamGenerator.fillCacheWithCustomKey(int datasetSize, IgniteCache<K,LabeledVector<Double>> cache, Function<LabeledVector<Double>,K> keyMapper) Fills given cache with labeled vectors from this generator and user defined mapper from vectors to keys.
default <K> void	DataStreamGenerator.fillCacheWithCustomKey(int datasetSize, IgniteCache<K,LabeledVector<Double>> cache, Function<LabeledVector<Double>,K> keyMapper) Fills given cache with labeled vectors from this generator and user defined mapper from vectors to keys.
default void	DataStreamGenerator.fillCacheWithVecHashAsKey(int datasetSize, IgniteCache<Integer,LabeledVector<Double>> cache) Fills given cache with labeled vectors from this generator as values and their hashcodes as keys.
default void	DataStreamGenerator.fillCacheWithVecUUIDAsKey(int datasetSize, IgniteCache<UUID,LabeledVector<Double>> cache) Fills given cache with labeled vectors from this generator as values and random UUIDs as keys

Uses of LabeledVector in org.apache.ignite.ml.util.generators.standard

Methods in org.apache.ignite.ml.util.generators.standard that return types with arguments of type LabeledVector

Modifier and Type	Method and Description
Stream<LabeledVector<Double>>	TwoSeparableClassesDataStream.labeled()
Stream<LabeledVector<Double>>	RegressionDataStream.labeled()
Stream<LabeledVector<Double>>	RingsDataStream.labeled()
Stream<LabeledVector<Double>>	GaussianMixtureDataStream.labeled()