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

Packages that use LabeledVector

Package	Description
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.classification	Contains main APIs for kNN classification algorithms.
org.apache.ignite.ml.knn.regression	Contains helper classes for kNN regression algorithms.
org.apache.ignite.ml.structures	Contains some internal utility structures.
org.apache.ignite.ml.structures.partition	Contains internal APIs for dataset partitioned labeled datasets.

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<Double,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<Double,LabeledVector>	NNClassificationModel.buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions)

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

Modifier and Type	Method and Description
protected LabeledVectorSet<Double,LabeledVector>	NNClassificationModel.buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions)
protected @NotNull TreeMap<Double,Set<Integer>>	NNClassificationModel.getDistances(Vector v, LabeledVectorSet<Double,LabeledVector> trainingData) Computes distances between given vector and each vector in training dataset.
protected @NotNull LabeledVector[]	NNClassificationModel.getKClosestVectors(LabeledVectorSet<Double,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<ProbableLabel,LabeledVector>	ANNModelFormat.getCandidates()
LabeledVectorSet<ProbableLabel,LabeledVector>	ANNClassificationModel.getCandidates()

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

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

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

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

Modifier and Type	Method and Description
protected List<LabeledVector>	KNNClassificationModel.findKNearestNeighbors(Vector v) The main idea is calculation all distance pairs between given vector and all vectors in training set, sorting them and finding k vectors with min distance with the given vector.

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

Constructor and Description
KNNClassificationModel(Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> dataset) Builds the model via prepared dataset.

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

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

Constructor and Description
KNNRegressionModel(Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> dataset) Builds the model via prepared dataset.

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<L,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<Double,LabeledVector>	LabeledDatasetPartitionDataBuilderOnHeap.build(Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx) Builds a new partition data from a partition upstream data and partition context