## Uses of Class
org.apache.ignite.ml.structures.LabeledVectorSet

- Packages that use LabeledVectorSet
  Package | Description
  --- | ---
  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.structures | Contains some internal utility structures.
  org.apache.ignite.ml.structures.partition | Contains internal APIs for dataset partitioned labeled datasets.
  org.apache.ignite.ml.structures.preprocessing | Contains internal APIs for dataset pre-processing.

- - ### Uses of LabeledVectorSet in org.apache.ignite.ml.knn

    Methods in org.apache.ignite.ml.knn that return LabeledVectorSet
    Modifier and Type | Method and Description
    --- | ---
    `protected LabeledVectorSet<LabeledVector>` | NNClassificationModel.`buildLabeledDatasetOnListOfVectors(List<LabeledVector> neighborsFromPartitions)`

    Methods in org.apache.ignite.ml.knn with parameters of type LabeledVectorSet
    Modifier and Type | Method and Description
    --- | ---
    `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 LabeledVectorSet in org.apache.ignite.ml.knn.ann

    Methods in org.apache.ignite.ml.knn.ann that return LabeledVectorSet
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVectorSet<LabeledVector>` | ANNClassificationModel.`getCandidates()`
    `LabeledVectorSet<LabeledVector>` | ANNModelFormat.`getCandidates()`

    Constructors in org.apache.ignite.ml.knn.ann with parameters of type LabeledVectorSet
    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 LabeledVectorSet in org.apache.ignite.ml.structures

    Methods in org.apache.ignite.ml.structures that return LabeledVectorSet
    Modifier and Type | Method and Description
    --- | ---
    `LabeledVectorSet` | LabeledVectorSet.`copy()` Makes copy with new Label objects and old features and Metadata objects.
    `LabeledVectorSet` | LabeledVectorSetTestTrainPair.`test()` Test subset of the whole dataset.
    `LabeledVectorSet` | LabeledVectorSetTestTrainPair.`train()` Train subset of the whole dataset.

    Constructors in org.apache.ignite.ml.structures with parameters of type LabeledVectorSet
    Constructor and Description
    `LabeledVectorSetTestTrainPair(LabeledVectorSet dataset, double testPercentage)` Creates two subsets of given dataset.

  - ### Uses of LabeledVectorSet in org.apache.ignite.ml.structures.partition

    Methods in org.apache.ignite.ml.structures.partition that return LabeledVectorSet
    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 LabeledVectorSet in org.apache.ignite.ml.structures.preprocessing

    Methods in org.apache.ignite.ml.structures.preprocessing that return LabeledVectorSet
    Modifier and Type | Method and Description
    --- | ---
    `static LabeledVectorSet` | LabeledDatasetLoader.`loadFromTxtFile(Path pathToFile, String separator, boolean isFallOnBadData)` Datafile should keep class labels in the first column.

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Release Date | : January 10 2022