## 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.exceptions.preprocessing | Contains exceptions for preprocessing.
  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.frequency | Contains frequency encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.label | Contains label encoding preprocessor.
  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.encoding.target | Contains frequency encoding preprocessor.
  org.apache.ignite.ml.preprocessing.finalization | Contains vector finalization 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.selection.scoring.evaluator.aggregator | Package contains aggregators for metrics computation.
  org.apache.ignite.ml.selection.scoring.evaluator.context | Package for metric evaluation contexts.
  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> 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 measure, 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 <L> List<LabeledVector<L>>` | PointWithDistanceUtil.`transformToListOrdered(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>>` | SpatialIndex.`findKClosest(int k, Vector pnt)` Finds `k` closest elements to the specified point.
    `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>>` | 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.exceptions.preprocessing

    Constructors in org.apache.ignite.ml.math.exceptions.preprocessing with parameters of type LabeledVector
    Constructor and Description
    `UndefinedLabelException(LabeledVector lv)`

  - ### 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.frequency

    Methods in org.apache.ignite.ml.preprocessing.encoding.frequency that return LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVector` | FrequencyEncoderPreprocessor.`apply(K k, V v)` Applies this preprocessor.

  - ### Uses of LabeledVector in org.apache.ignite.ml.preprocessing.encoding.label

    Methods in org.apache.ignite.ml.preprocessing.encoding.label that return LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVector` | LabelEncoderPreprocessor.`apply(K k, V v)` Applies this 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.encoding.target

    Methods in org.apache.ignite.ml.preprocessing.encoding.target that return LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVector` | TargetEncoderPreprocessor.`apply(K k, V v)` Applies this preprocessor.

  - ### Uses of LabeledVector in org.apache.ignite.ml.preprocessing.finalization

    Methods in org.apache.ignite.ml.preprocessing.finalization that return LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVector` | VectorFinalizationPreprocessor.`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.selection.scoring.evaluator.aggregator

    Methods in org.apache.ignite.ml.selection.scoring.evaluator.aggregator with parameters of type LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `void` | RegressionMetricStatsAggregator.`aggregate(IgniteModel<Vector,Double> model, LabeledVector<Double> vector)` Aggregates statistics for metric computation given model and vector with answer.
    `void` | BinaryClassificationPointwiseMetricStatsAggregator.`aggregate(IgniteModel<Vector,L> mdl, LabeledVector<L> vector)` Aggregates statistics for metric computation given model and vector with answer.
    `void` | ClassificationMetricsAggregator.`aggregate(IgniteModel<Vector,L> mdl, LabeledVector<L> vector)` Aggregates statistics for metric computation given model and vector with answer.
    `void` | MetricStatsAggregator.`aggregate(IgniteModel<Vector,L> model, LabeledVector<L> vector)` Aggregates statistics for metric computation given model and vector with answer.

  - ### Uses of LabeledVector in org.apache.ignite.ml.selection.scoring.evaluator.context

    Methods in org.apache.ignite.ml.selection.scoring.evaluator.context with parameters of type LabeledVector
    Modifier and Type | Method and Description
    --- | ---
    `void` | BinaryClassificationEvaluationContext.`aggregate(LabeledVector<L> vector)` Aggregates statistic from vector of sample.
    `void` | EmptyContext.`aggregate(LabeledVector<L> vector)` Aggregates statistic from vector of sample.
    `void` | EvaluationContext.`aggregate(LabeledVector<L> vector)` Aggregates statistic from vector of sample.

  - ### 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 this 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 this 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>>` | RegressionDataStream.`labeled()`
    `Stream<LabeledVector<Double>>` | RingsDataStream.`labeled()`
    `Stream<LabeledVector<Double>>` | GaussianMixtureDataStream.`labeled()`
    `Stream<LabeledVector<Double>>` | TwoSeparableClassesDataStream.`labeled()`

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