## Uses of Interface
org.apache.ignite.ml.math.primitives.vector.Vector

- Packages that use Vector
  Package | Description
  --- | ---
  org.apache.ignite.ml.clustering.gmm | Contains Gauss Mixture Model clustering algorithm (see `GmmModel`).
  org.apache.ignite.ml.clustering.kmeans | Contains kMeans clustering algorithm.
  org.apache.ignite.ml.composition | Contains classes for ensemble of models implementation.
  org.apache.ignite.ml.composition.bagging | Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and return a bagged version of them.
  org.apache.ignite.ml.composition.boosting | Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.
  org.apache.ignite.ml.composition.boosting.convergence | Package contains implementation of convergence checking algorithms for gradient boosting.
  org.apache.ignite.ml.composition.stacking | Contains classes used for training with stacking technique.
  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.environment.logging | Package contains several logging strategy realisations.
  org.apache.ignite.ml.inference | Root package for model inference functionality.
  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.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 | Contains main APIs for matrix/vector algebra.
  org.apache.ignite.ml.math.distances | Contains main APIs for distances.
  org.apache.ignite.ml.math.exceptions.preprocessing | Contains exceptions for preprocessing.
  org.apache.ignite.ml.math.functions | Contains serializable functions for distributed code algebra.
  org.apache.ignite.ml.math.primitives.matrix | Contains matrix related classes.
  org.apache.ignite.ml.math.primitives.matrix.impl | Contains several matrix implementations.
  org.apache.ignite.ml.math.primitives.vector | Contains vector related classes.
  org.apache.ignite.ml.math.primitives.vector.impl | Contains several vector implementations.
  org.apache.ignite.ml.math.stat | Contains utility classes for distributions.
  org.apache.ignite.ml.math.util | Some math utils.
  org.apache.ignite.ml.multiclass | Contains various multi-classifier models and trainers.
  org.apache.ignite.ml.naivebayes.compound | Contains Compound naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.discrete | Contains Bernoulli naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.gaussian | Contains Gaussian naive Bayes classifier.
  org.apache.ignite.ml.nn | Contains neural networks and related classes.
  org.apache.ignite.ml.nn.initializers | Contains multilayer perceptron parameters initializers.
  org.apache.ignite.ml.optimization | Contains implementations of optimization algorithms and related classes.
  org.apache.ignite.ml.optimization.updatecalculators | Contains update calculators.
  org.apache.ignite.ml.pipeline | Contains Pipeline API.
  org.apache.ignite.ml.preprocessing.imputing | Contains Imputer preprocessor.
  org.apache.ignite.ml.recommendation | Contains recommendation system framework.
  org.apache.ignite.ml.recommendation.util | Contains util classes used in recommendation system framework.
  org.apache.ignite.ml.regressions.linear | Contains various linear regressions.
  org.apache.ignite.ml.regressions.logistic | Contains various logistic regressions.
  org.apache.ignite.ml.selection.cv | Root package for cross-validation algorithms.
  org.apache.ignite.ml.selection.scoring.cursor | Util classes used for score calculation.
  org.apache.ignite.ml.selection.scoring.evaluator | Package for model evaluator classes.
  org.apache.ignite.ml.selection.scoring.evaluator.aggregator | Package contains aggregators for metrics computation.
  org.apache.ignite.ml.structures | Contains some internal utility structures.
  org.apache.ignite.ml.svm | Contains main APIs for SVM(support vector machines) algorithms.
  org.apache.ignite.ml.trainers | Contains model trainers.
  org.apache.ignite.ml.tree | Root package for decision trees.
  org.apache.ignite.ml.tree.boosting | Contains implementation of gradient boosting on trees.
  org.apache.ignite.ml.tree.impurity | Root package for decision tree impurity measures and calculators.
  org.apache.ignite.ml.tree.randomforest.data | Package contains helper data structures for random forest implementation.
  org.apache.ignite.ml.util.generators | Contains utility classes for data streams generation.
  org.apache.ignite.ml.util.generators.primitives.scalar | Contains generators of pseudo-random scalars in according to specific distribution.
  org.apache.ignite.ml.util.generators.primitives.vector | Contains generators of pseudo-random vectors in according to specific distribution.
  org.apache.ignite.ml.util.generators.standard | Contains classes for predefined data stream generators.

- - ### Uses of Vector in org.apache.ignite.ml.clustering.gmm

    Methods in org.apache.ignite.ml.clustering.gmm that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | NewComponentStatisticsAggregator.`mean()`

    Methods in org.apache.ignite.ml.clustering.gmm with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | GmmModel.`predict(Vector input)` Make a prediction for the specified input arguments.

    Method parameters in org.apache.ignite.ml.clustering.gmm with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `GmmTrainer` | GmmTrainer.`withInitialMeans(List<Vector> means)` Sets initial means.

    Constructors in org.apache.ignite.ml.clustering.gmm with parameters of type Vector
    Constructor and Description
    `GmmModel(Vector componentProbs, List<MultivariateGaussianDistribution> distributions)` Creates an instance of GmmModel.
    `NewComponentStatisticsAggregator(long totalRowCount, long rowCountForNewCluster, Vector sumOfAnomalies)` Creates an instance of NewComponentStatisticsAggregator.

  - ### Uses of Vector in org.apache.ignite.ml.clustering.kmeans

    Methods in org.apache.ignite.ml.clustering.kmeans that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector[]` | KMeansModel.`centers()` Get cluster centers.
    `Vector[]` | KMeansModelFormat.`getCenters()`

    Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Integer` | KMeansModel.`predict(Vector vec)` Make a prediction for the specified input arguments.
    `KMeansModel` | KMeansModel.`withCentroids(Vector[] centers)` Set up the centroids.

    Constructors in org.apache.ignite.ml.clustering.kmeans with parameters of type Vector
    Constructor and Description
    `KMeansModel(Vector[] centers, DistanceMeasure distanceMeasure)` Construct KMeans model with given centers and distanceMeasure measure.
    `KMeansModelFormat(Vector[] centers, DistanceMeasure distance)`

  - ### Uses of Vector in org.apache.ignite.ml.composition

    Classes in org.apache.ignite.ml.composition with type parameters of type Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ModelsComposition<M extends IgniteModel<Vector,Double>>` Model consisting of several models and prediction aggregation strategy.

    Methods in org.apache.ignite.ml.composition that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `default Vector` | DatasetMapping.`mapFeatures(Vector v)` Method used to map feature vectors.

    Methods in org.apache.ignite.ml.composition that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V,L> IgniteBiFunction<K,V,Vector>` | CompositionUtils.`asFeatureExtractor(FeatureLabelExtractor<K,V,L> extractor)` Create feature extractor from given mapping `(key, value) -> LabeledVector`.
    `IgniteModel<Vector,Double>` | ModelOnFeaturesSubspace.`getMdl()` Returns model.
    `List<? extends IgniteModel<Vector,Double>>` | ModelsCompositionFormat.`models()`

    Methods in org.apache.ignite.ml.composition with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `default Vector` | DatasetMapping.`mapFeatures(Vector v)` Method used to map feature vectors.
    `Double` | ModelsComposition.`predict(Vector features)` Applies containing models to features and aggregate them to one prediction.
    `Double` | ModelOnFeaturesSubspace.`predict(Vector features)` Projects features vector to subspace in according to mapping and apply model to it.

    Method parameters in org.apache.ignite.ml.composition with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static <L> DatasetMapping<L,L>` | DatasetMapping.`mappingFeatures(IgniteFunction<Vector,Vector> mapper)` Dataset mapping which maps features, leaving labels unaffected.
    `static <L> DatasetMapping<L,L>` | DatasetMapping.`mappingFeatures(IgniteFunction<Vector,Vector> mapper)` Dataset mapping which maps features, leaving labels unaffected.

    Constructor parameters in org.apache.ignite.ml.composition with type arguments of type Vector
    Constructor and Description
    `ModelsCompositionFormat(List<? extends IgniteModel<Vector,Double>> models, PredictionsAggregator predictionsAggregator)` Creates an instance of ModelsCompositionFormat.

  - ### Uses of Vector in org.apache.ignite.ml.composition.bagging

    Methods in org.apache.ignite.ml.composition.bagging with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | BaggedModel.`predict(Vector i)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.composition.boosting

    Fields in org.apache.ignite.ml.composition.boosting with type parameters of type Vector
    Modifier and Type | Field and Description
    --- | ---
    `protected IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>>` | GDBLearningStrategy.`baseMdlTrainerBuilder` Base model trainer builder.

    Methods in org.apache.ignite.ml.composition.boosting that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> List<IgniteModel<Vector,Double>>` | GDBLearningStrategy.`learnModels(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Implementation of gradient boosting iterations.
    `<K,V> List<IgniteModel<Vector,Double>>` | GDBLearningStrategy.`update(GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

    Methods in org.apache.ignite.ml.composition.boosting with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | GDBModel.`predict(Vector features)` Applies containing models to features and aggregate them to one prediction.

    Method parameters in org.apache.ignite.ml.composition.boosting with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `GDBLearningStrategy` | GDBLearningStrategy.`withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> buildBaseMdlTrainer)` Sets base model builder.

    Constructor parameters in org.apache.ignite.ml.composition.boosting with type arguments of type Vector
    Constructor and Description
    `GDBModel(List<? extends IgniteModel<Vector,Double>> models, WeightedPredictionsAggregator predictionsAggregator, IgniteFunction<Double,Double> internalToExternalLblMapping)` Creates an instance of GDBModel.

  - ### Uses of Vector in org.apache.ignite.ml.composition.boosting.convergence

    Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `double` | ConvergenceChecker.`computeError(Vector features, Double answer, ModelsComposition currMdl)` Compute error for the specific vector of dataset.

  - ### Uses of Vector in org.apache.ignite.ml.composition.stacking

    Classes in org.apache.ignite.ml.composition.stacking with type parameters of type Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L>` `StackedDatasetTrainer` with `Vector` as submodels input and output.

    Methods in org.apache.ignite.ml.composition.stacking with type parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `<M1 extends IgniteModel<Vector,Vector>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<M1 extends IgniteModel<Vector,Vector>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<M1 extends IgniteModel<Vector,Double>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addTrainerWithDoubleOutput(DatasetTrainer<M1,L> trainer)` Shortcut for adding trainer `Vector -> Double` where this trainer is treated as `Vector -> Vector`, where output `Vector` is constructed by wrapping double value.

    Method parameters in org.apache.ignite.ml.composition.stacking with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<Vector> merger)` Specify binary operator used to merge submodels outputs to one.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withOriginalFeaturesKept(IgniteFunction<Vector,Vector> submodelInput2AggregatingInputConverter)` Keep original features during training and propagate submodels input to aggregator during inference using given function.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withOriginalFeaturesKept(IgniteFunction<Vector,Vector> submodelInput2AggregatingInputConverter)` Keep original features during training and propagate submodels input to aggregator during inference using given function.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | StackedDatasetTrainer.`withSubmodelOutput2VectorConverter(IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Set function used for conversion of submodel output to `Vector`.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withSubmodelOutput2VectorConverter(IgniteFunction<Vector,Vector> submodelOutput2VectorConverter)` Set function used for conversion of submodel output to `Vector`.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withSubmodelOutput2VectorConverter(IgniteFunction<Vector,Vector> submodelOutput2VectorConverter)` Set function used for conversion of submodel output to `Vector`.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | StackedDatasetTrainer.`withVector2SubmodelInputConverter(IgniteFunction<Vector,IS> vector2SubmodelInputConverter)` Set function used for conversion of `Vector` to submodel input.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withVector2SubmodelInputConverter(IgniteFunction<Vector,Vector> vector2SubmodelInputConverter)` Set function used for conversion of `Vector` to submodel input.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withVector2SubmodelInputConverter(IgniteFunction<Vector,Vector> vector2SubmodelInputConverter)` Set function used for conversion of `Vector` to submodel input.

    Constructor parameters in org.apache.ignite.ml.composition.stacking with type arguments of type Vector
    Constructor and Description
    `SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger, IgniteFunction<I,I> submodelInput2AggregatingInputConverter, IgniteFunction<Vector,I> vector2SubmodelInputConverter, IgniteFunction<I,Vector> submodelOutput2VectorConverter)` Construct instance of this class.
    `SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger, IgniteFunction<I,I> submodelInput2AggregatingInputConverter, IgniteFunction<Vector,I> vector2SubmodelInputConverter, IgniteFunction<I,Vector> submodelOutput2VectorConverter)` Construct instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Create instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Create instance of this class.

  - ### Uses of Vector in org.apache.ignite.ml.dataset.feature.extractor

    Methods in org.apache.ignite.ml.dataset.feature.extractor that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `protected Vector` | Vectorizer.`createVector(int size)` Create an instance of vector.

  - ### Uses of Vector in org.apache.ignite.ml.dataset.feature.extractor.impl

    Methods in org.apache.ignite.ml.dataset.feature.extractor.impl that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `protected Vector` | BinaryObjectVectorizer.`createVector(int size)` Create an instance of vector.

    Methods in org.apache.ignite.ml.dataset.feature.extractor.impl with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `protected Serializable` | DummyVectorizer.`feature(Integer coord, K key, Vector val)` Extracts feature value by given coordinate.
    `protected int` | DummyVectorizer.`sizeOf(K key, Vector val)` Size of array-like structure of upstream object.

  - ### Uses of Vector in org.apache.ignite.ml.dataset.impl.bootstrapping

    Constructors in org.apache.ignite.ml.dataset.impl.bootstrapping with parameters of type Vector
    Constructor and Description
    `BootstrappedVector(Vector features, double lb, int[] counters)` Creates an instance of BootstrappedVector.

  - ### Uses of Vector in org.apache.ignite.ml.environment.logging

    Methods in org.apache.ignite.ml.environment.logging that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | CustomMLLogger.`log(Vector vector)` Log vector.
    `Vector` | NoOpLogger.`log(Vector vector)` Log vector.
    `Vector` | MLLogger.`log(Vector vector)` Log vector.
    `Vector` | ConsoleLogger.`log(Vector vector)` Log vector.

    Methods in org.apache.ignite.ml.environment.logging with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | CustomMLLogger.`log(Vector vector)` Log vector.
    `Vector` | NoOpLogger.`log(Vector vector)` Log vector.
    `Vector` | MLLogger.`log(Vector vector)` Log vector.
    `Vector` | ConsoleLogger.`log(Vector vector)` Log vector.

  - ### Uses of Vector in org.apache.ignite.ml.inference

    Methods in org.apache.ignite.ml.inference that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static Model<Vector,Future<Double>>` | IgniteModelStorageUtil.`getAsyncModel(Ignite ignite, String name, AsyncModelBuilder mdlBldr)` Retrieves Ignite model by name using asynchronous model builder.

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

    Methods in org.apache.ignite.ml.knn with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `List<LabeledVector<L>>` | KNNModel.`findKClosest(int k, Vector pnt)` Finds `k` closest elements to the specified point.
    `protected @NotNull TreeMap<Double,Set<Integer>>` | NNClassificationModel.`getDistances(Vector v, LabeledVectorSet<LabeledVector> trainingData)` Computes distances between given vector and each vector in training dataset.

  - ### Uses of Vector in org.apache.ignite.ml.knn.ann

    Methods in org.apache.ignite.ml.knn.ann with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | ANNClassificationModel.`predict(Vector v)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.knn.classification

    Methods in org.apache.ignite.ml.knn.classification with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | KNNClassificationModel.`predict(Vector input)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.knn.regression

    Methods in org.apache.ignite.ml.knn.regression with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | KNNRegressionModel.`predict(Vector input)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.knn.utils

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

  - ### Uses of Vector in org.apache.ignite.ml.knn.utils.indices

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

  - ### Uses of Vector in org.apache.ignite.ml.math

    Methods in org.apache.ignite.ml.math with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static String` | Tracer.`asAscii(Vector vec, String fmt, boolean showMeta)`
    `static void` | Blas.`axpy(Double a, Vector x, Vector y)` Performs y += a * x
    `static void` | Blas.`checkCardinality(Matrix a, Vector v)` Checks if Matrix A can be multiplied by vector v, if not CardinalityException is thrown.
    `void` | Blas.`copy(Vector x, Vector y)` Copies Vector x into Vector y.
    `static Double` | Blas.`dot(Vector x, Vector y)` Returns dot product of vectors x and y.
    `static void` | Blas.`gemv(double alpha, Matrix a, Vector x, double beta, Vector y)` y := alpha * A * x + beta * y.
    `static void` | Tracer.`saveAsCsv(Vector vec, String fmt, String filePath)` Saves given vector as CSV file.
    `static void` | Blas.`scal(Double a, Vector x)` Performs in-place multiplication of vector x by a real scalar a.
    `static void` | Tracer.`showAscii(Vector vec)`
    `static void` | Tracer.`showAscii(Vector vec, IgniteLogger log)`
    `static void` | Tracer.`showAscii(Vector vec, IgniteLogger log, String fmt)`
    `static void` | Tracer.`showAscii(Vector vec, String fmt)`
    `static void` | Tracer.`showHtml(Vector vec)` Shows given vector in the browser with D3-based visualization.
    `static void` | Tracer.`showHtml(Vector vec, boolean useAsciiFallback)` Shows given vector in the browser with D3-based visualization.
    `static void` | Tracer.`showHtml(Vector vec, Tracer.ColorMapper cm)` Shows given vector in the browser with D3-based visualization.
    `static void` | Tracer.`showHtml(Vector vec, Tracer.ColorMapper cm, boolean useAsciiFallback)` Shows given vector in the browser with D3-based visualization.

  - ### Uses of Vector in org.apache.ignite.ml.math.distances

    Methods in org.apache.ignite.ml.math.distances with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `double` | EuclideanDistance.`compute(Vector a, double[] b)` Compute the distance between n-dimensional vector and n-dimensional array.
    `default double` | DistanceMeasure.`compute(Vector a, double[] b)` Compute the distance between n-dimensional vector and n-dimensional array.
    `double` | CosineSimilarity.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | EuclideanDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | JensenShannonDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | ChebyshevDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | JaccardIndex.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | CanberraDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | DistanceMeasure.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | ManhattanDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | HammingDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | WeightedMinkowskiDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | BrayCurtisDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.
    `double` | MinkowskiDistance.`compute(Vector a, Vector b)` Compute the distance between two n-dimensional vectors.

  - ### Uses of Vector in org.apache.ignite.ml.math.exceptions.preprocessing

    Constructors in org.apache.ignite.ml.math.exceptions.preprocessing with parameters of type Vector
    Constructor and Description
    `NonDoubleVectorException(Vector row)`

  - ### Uses of Vector in org.apache.ignite.ml.math.functions

    Methods in org.apache.ignite.ml.math.functions that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | IgniteDifferentiableVectorToDoubleFunction.`differential(Vector pnt)` Get function differential at a given point.

    Methods in org.apache.ignite.ml.math.functions with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | IgniteDifferentiableVectorToDoubleFunction.`differential(Vector pnt)` Get function differential at a given point.

  - ### Uses of Vector in org.apache.ignite.ml.math.primitives.matrix

    Methods in org.apache.ignite.ml.math.primitives.matrix that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | Matrix.`foldColumns(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all columns in this matrix.
    `Vector` | AbstractMatrix.`foldColumns(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all columns in this matrix.
    `Vector` | Matrix.`foldRows(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all rows in this matrix.
    `Vector` | AbstractMatrix.`foldRows(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all rows in this matrix.
    `Vector` | Matrix.`getCol(int col)` Get a specific row from matrix.
    `Vector` | AbstractMatrix.`getCol(int col)` Get a specific row from matrix.
    `Vector` | LUDecomposition.`getPivot()` Returns the pivot permutation vector.
    `Vector` | Matrix.`getRow(int row)` Get a specific row from matrix.
    `Vector` | AbstractMatrix.`getRow(int row)` Get a specific row from matrix.
    `Vector` | Matrix.`likeVector(int crd)` Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
    `Vector` | LUDecomposition.`solve(Vector b)`
    `Vector` | Matrix.`times(Vector vec)` Creates new matrix that is the product of multiplying this matrix and the argument vector.
    `Vector` | AbstractMatrix.`times(Vector vec)` Creates new matrix that is the product of multiplying this matrix and the argument vector.
    `Vector` | Matrix.`viewColumn(int col)` Creates new view into matrix column .
    `Vector` | AbstractMatrix.`viewColumn(int col)` Creates new view into matrix column .
    `Vector` | Matrix.`viewDiagonal()` Creates new view into matrix diagonal.
    `Vector` | AbstractMatrix.`viewDiagonal()` Creates new view into matrix diagonal.
    `Vector` | Matrix.`viewRow(int row)` Creates new view into matrix row.
    `Vector` | AbstractMatrix.`viewRow(int row)` Creates new view into matrix row.

    Methods in org.apache.ignite.ml.math.primitives.matrix with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Matrix` | Matrix.`assignColumn(int col, Vector vec)` Assigns values from given vector to the specified column in this matrix.
    `Matrix` | AbstractMatrix.`assignColumn(int col, Vector vec)` Assigns values from given vector to the specified column in this matrix.
    `Matrix` | Matrix.`assignRow(int row, Vector vec)` Assigns values from given vector to the specified row in this matrix.
    `Matrix` | AbstractMatrix.`assignRow(int row, Vector vec)` Assigns values from given vector to the specified row in this matrix.
    `Vector` | LUDecomposition.`solve(Vector b)`
    `Vector` | Matrix.`times(Vector vec)` Creates new matrix that is the product of multiplying this matrix and the argument vector.
    `Vector` | AbstractMatrix.`times(Vector vec)` Creates new matrix that is the product of multiplying this matrix and the argument vector.

    Method parameters in org.apache.ignite.ml.math.primitives.matrix with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | Matrix.`foldColumns(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all columns in this matrix.
    `Vector` | AbstractMatrix.`foldColumns(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all columns in this matrix.
    `Vector` | Matrix.`foldRows(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all rows in this matrix.
    `Vector` | AbstractMatrix.`foldRows(IgniteFunction<Vector,Double> fun)` Collects the results of applying a given function to all rows in this matrix.

  - ### Uses of Vector in org.apache.ignite.ml.math.primitives.matrix.impl

    Methods in org.apache.ignite.ml.math.primitives.matrix.impl that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | SparseMatrix.`likeVector(int crd)` Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
    `Vector` | ViewMatrix.`likeVector(int crd)` Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
    `Vector` | DenseMatrix.`likeVector(int crd)` Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.

  - ### Uses of Vector in org.apache.ignite.ml.math.primitives.vector

    Subinterfaces of Vector in org.apache.ignite.ml.math.primitives.vector
    Modifier and Type | Interface and Description
    --- | ---
    `interface` | `NamedVector` A named vector interface based on `Vector`.

    Classes in org.apache.ignite.ml.math.primitives.vector that implement Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `AbstractVector` This class provides a helper implementation of the `Vector` interface to minimize the effort required to implement it.

    Methods in org.apache.ignite.ml.math.primitives.vector that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | Vector.`assign(double val)` Assigns given value to all elements of this vector.
    `Vector` | AbstractVector.`assign(double val)` Assigns given value to all elements of this vector.
    `Vector` | Vector.`assign(double[] vals)` Assigns values from given array to this vector.
    `Vector` | AbstractVector.`assign(double[] vals)` Assigns values from given array to this vector.
    `Vector` | Vector.`assign(IntToDoubleFunction fun)` Assigns each vector element to the value generated by given function.
    `Vector` | AbstractVector.`assign(IntToDoubleFunction fun)` Assigns each vector element to the value generated by given function.
    `Vector` | Vector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `Vector` | AbstractVector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `static Vector` | VectorUtils.`concat(Vector... vs)` Concatenates given vectors.
    `static Vector` | VectorUtils.`concat(Vector v1, Vector... vs)` Concatenates given vectors.
    `static Vector` | VectorUtils.`concat(Vector v1, Vector v2)` Concatenates two given vectors.
    `Vector` | Vector.`copy()` Creates new copy of this vector.
    `Vector` | AbstractVector.`copy()` Creates new copy of this vector.
    `Vector` | Vector.`copyOfRange(int from, int to)` Copies the specified range of the vector into a new vector.
    `Vector` | AbstractVector.`copyOfRange(int from, int to)` Copies the specified range of the vector into a new vector.
    `static Vector` | VectorUtils.`copyPart(Vector v, int off, int len)` Get copy of part of given length of given vector starting from given offset.
    `Vector` | Vector.`divide(double x)` Creates new vector containing values from this vector divided by the argument.
    `Vector` | AbstractVector.`divide(double x)` Creates new vector containing values from this vector divided by the argument.
    `static Vector` | VectorUtils.`elementWiseMinus(Vector vec1, Vector vec2)` Performs in-place vector subtraction.
    `static Vector` | VectorUtils.`elementWiseTimes(Vector vec1, Vector vec2)` Performs in-place vector multiplication.
    `Vector` | Vector.`increment(int idx, double val)` Increments value at given index.
    `Vector` | AbstractVector.`increment(int idx, double val)` Increments value at given index.
    `Vector` | Vector.`incrementX(int idx, double val)` Increments value at given index without checking for index boundaries.
    `Vector` | AbstractVector.`incrementX(int idx, double val)` Increments value at given index without checking for index boundaries.
    `Vector` | Vector.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | Vector.`logNormalize()` Creates new vector containing the `log(1 + entry) / L_2 norm` values of this vector.
    `Vector` | AbstractVector.`logNormalize()` Creates new vector containing the `log(1 + entry) / L_2 norm` values of this vector.
    `Vector` | Vector.`logNormalize(double power)` Creates new vector with a normalized value calculated as `log_power(1 + entry) / L_power norm`.
    `Vector` | AbstractVector.`logNormalize(double power)` Creates new vector with a normalized value calculated as `log_power(1 + entry) / L_power norm`.
    `Vector` | Vector.`map(IgniteBiFunction<Double,Double,Double> fun, double y)` Maps all elements of this vector by applying given function to each element with a constant second parameter `y`.
    `Vector` | AbstractVector.`map(IgniteBiFunction<Double,Double,Double> fun, double y)` Maps all elements of this vector by applying given function to each element with a constant second parameter `y`.
    `Vector` | Vector.`map(IgniteDoubleFunction<Double> fun)` Maps all values in this vector through a given function.
    `Vector` | AbstractVector.`map(IgniteDoubleFunction<Double> fun)` Maps all values in this vector through a given function.
    `Vector` | Vector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | AbstractVector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | Vector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | AbstractVector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | Vector.`normalize()` Creates new vector containing the normalized (L_2 norm) values of this vector.
    `Vector` | AbstractVector.`normalize()` Creates new vector containing the normalized (L_2 norm) values of this vector.
    `Vector` | Vector.`normalize(double power)` Creates new vector containing the normalized (L_power norm) values of this vector.
    `Vector` | AbstractVector.`normalize(double power)` Creates new vector containing the normalized (L_power norm) values of this vector.
    `static Vector` | VectorUtils.`num2Vec(double val)` Wrap specified value into vector.
    `static Vector` | VectorUtils.`of(double... values)` Creates dense local on heap vector based on array of doubles.
    `static Vector` | VectorUtils.`of(Double[] values)` Creates vector based on array of Doubles.
    `Vector` | Vector.`plus(double x)` Creates new vector containing sum of each element in this vector and argument.
    `Vector` | AbstractVector.`plus(double x)` Creates new vector containing sum of each element in this vector and argument.
    `Vector` | Vector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | AbstractVector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | Vector.`set(int idx, double val)` Sets value.
    `Vector` | AbstractVector.`set(int idx, double val)` Sets value.
    `Vector` | Vector.`setRaw(int idx, Serializable val)` Sets value.
    `Vector` | AbstractVector.`setRaw(int idx, Serializable val)` Sets value.
    `Vector` | Vector.`setRawX(int idx, Serializable val)` Sets value without checking for index boundaries.
    `Vector` | AbstractVector.`setRawX(int idx, Serializable val)` Sets value without checking for index boundaries.
    `Vector` | Vector.`setX(int idx, double val)` Sets value without checking for index boundaries.
    `Vector` | AbstractVector.`setX(int idx, double val)` Sets value without checking for index boundaries.
    `Vector` | Vector.`sort()` Sorts this vector in ascending order.
    `Vector` | AbstractVector.`sort()` Sorts this vector in ascending order.
    `Vector` | Vector.`times(double x)` Gets a new vector that contains product of each element and the argument.
    `Vector` | AbstractVector.`times(double x)` Gets a new vector that contains product of each element and the argument.
    `Vector` | Vector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.
    `Vector` | AbstractVector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.
    `Vector` | Vector.`viewPart(int off, int len)`
    `Vector` | AbstractVector.`viewPart(int off, int len)`
    `static Vector` | VectorUtils.`zeroesLike(Vector v)` Create new vector like given vector initialized by zeroes.
    `static Vector` | VectorUtils.`zipWith(Vector v1, Vector v2, IgniteBiFunction<Double,Double,Double> f)` Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

    Methods in org.apache.ignite.ml.math.primitives.vector that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static IgniteFunction<Vector,Vector>` | VectorUtils.`getProjector(int[] mapping)` Get projector from index mapping.
    `static IgniteFunction<Vector,Vector>` | VectorUtils.`getProjector(int[] mapping)` Get projector from index mapping.

    Methods in org.apache.ignite.ml.math.primitives.vector with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | Vector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `Vector` | AbstractVector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `protected void` | AbstractVector.`checkCardinality(Vector vec)`
    `static Vector` | VectorUtils.`concat(Vector... vs)` Concatenates given vectors.
    `static Vector` | VectorUtils.`concat(Vector v1, Vector... vs)` Concatenates given vectors.
    `static Vector` | VectorUtils.`concat(Vector v1, Vector... vs)` Concatenates given vectors.
    `static Vector` | VectorUtils.`concat(Vector v1, Vector v2)` Concatenates two given vectors.
    `static Vector` | VectorUtils.`copyPart(Vector v, int off, int len)` Get copy of part of given length of given vector starting from given offset.
    `Matrix` | Vector.`cross(Vector vec)` Gets the cross product of this vector and the other vector.
    `Matrix` | AbstractVector.`cross(Vector vec)` Gets the cross product of this vector and the other vector.
    `double` | Vector.`dot(Vector vec)` Gets dot product of two vectors.
    `double` | AbstractVector.`dot(Vector vec)` Gets dot product of two vectors.
    `static Vector` | VectorUtils.`elementWiseMinus(Vector vec1, Vector vec2)` Performs in-place vector subtraction.
    `static Vector` | VectorUtils.`elementWiseTimes(Vector vec1, Vector vec2)` Performs in-place vector multiplication.
    `<T> T` | Vector.`foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal)` Combines & maps two vector and folds them into a single value.
    `<T> T` | AbstractVector.`foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal)` Combines & maps two vector and folds them into a single value.
    `double` | Vector.`getDistanceSquared(Vector vec)` Get the square of the distance between this vector and the argument vector.
    `double` | AbstractVector.`getDistanceSquared(Vector vec)` Get the square of the distance between this vector and the argument vector.
    `Vector` | Vector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | AbstractVector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | Vector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | AbstractVector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | Vector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | AbstractVector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | Vector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.
    `Vector` | AbstractVector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.
    `static double` | VectorUtils.`vec2Num(Vector vec)` Turn Vector into number by looking at index of maximal element in vector.
    `static Vector` | VectorUtils.`zeroesLike(Vector v)` Create new vector like given vector initialized by zeroes.
    `static Vector` | VectorUtils.`zipWith(Vector v1, Vector v2, IgniteBiFunction<Double,Double,Double> f)` Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

  - ### Uses of Vector in org.apache.ignite.ml.math.primitives.vector.impl

    Classes in org.apache.ignite.ml.math.primitives.vector.impl that implement Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DelegatingNamedVector` Delegating named vector that delegates all operations to underlying vector and adds implementation of `NamedVector` functionality using embedded map that maps string index on real integer index.
    `class` | `DelegatingVector` Convenient class that can be used to add decorations to an existing vector.
    `class` | `DenseVector` Basic implementation for vector.
    `class` | `SparseVector` Local on-heap sparse vector based on hash map storage.
    `class` | `VectorizedViewMatrix` Row or column vector view off the matrix.
    `class` | `VectorView` Implements the partial view into the parent `Vector`.

    Methods in org.apache.ignite.ml.math.primitives.vector.impl that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | DelegatingVector.`assign(double val)` Assigns given value to all elements of this vector.
    `Vector` | DelegatingVector.`assign(double[] vals)` Assigns values from given array to this vector.
    `Vector` | DelegatingVector.`assign(IntToDoubleFunction fun)` Assigns each vector element to the value generated by given function.
    `Vector` | DelegatingVector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `Vector` | DelegatingVector.`copy()` Creates new copy of this vector.
    `Vector` | VectorView.`copy()` Creates new copy of this vector.
    `Vector` | VectorizedViewMatrix.`copy()` Creates new copy of this vector.
    `Vector` | DelegatingVector.`copyOfRange(int from, int to)` Copies the specified range of the vector into a new vector.
    `Vector` | DelegatingVector.`divide(double x)` Creates new vector containing values from this vector divided by the argument.
    `Vector` | DelegatingVector.`getVector()` Get the delegating vector
    `Vector` | DelegatingVector.`increment(int idx, double val)` Increments value at given index.
    `Vector` | DelegatingVector.`incrementX(int idx, double val)` Increments value at given index without checking for index boundaries.
    `Vector` | DelegatingVector.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | VectorView.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | SparseVector.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | DenseVector.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | VectorizedViewMatrix.`like(int crd)` Creates new empty vector of the same underlying class but of different cardinality.
    `Vector` | DelegatingVector.`logNormalize()` Creates new vector containing the `log(1 + entry) / L_2 norm` values of this vector.
    `Vector` | DelegatingVector.`logNormalize(double power)` Creates new vector with a normalized value calculated as `log_power(1 + entry) / L_power norm`.
    `Vector` | DelegatingVector.`map(IgniteBiFunction<Double,Double,Double> fun, double y)` Maps all elements of this vector by applying given function to each element with a constant second parameter `y`.
    `Vector` | DelegatingVector.`map(IgniteDoubleFunction<Double> fun)` Maps all values in this vector through a given function.
    `Vector` | DelegatingVector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | DelegatingVector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | DelegatingVector.`normalize()` Creates new vector containing the normalized (L_2 norm) values of this vector.
    `Vector` | DelegatingVector.`normalize(double power)` Creates new vector containing the normalized (L_power norm) values of this vector.
    `Vector` | DelegatingVector.`plus(double x)` Creates new vector containing sum of each element in this vector and argument.
    `Vector` | DelegatingVector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | DelegatingVector.`set(int idx, double val)` Sets value.
    `Vector` | DelegatingVector.`setRaw(int idx, Serializable val)` Sets value.
    `Vector` | DelegatingVector.`setRawX(int idx, Serializable val)` Sets value without checking for index boundaries.
    `Vector` | DelegatingVector.`setX(int idx, double val)` Sets value without checking for index boundaries.
    `Vector` | DelegatingVector.`sort()` Sorts this vector in ascending order.
    `Vector` | DelegatingVector.`times(double x)` Gets a new vector that contains product of each element and the argument.
    `Vector` | SparseVector.`times(double x)` Gets a new vector that contains product of each element and the argument.
    `Vector` | DelegatingVector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.
    `Vector` | DelegatingVector.`viewPart(int off, int len)`

    Methods in org.apache.ignite.ml.math.primitives.vector.impl with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | DelegatingVector.`assign(Vector vec)` Copies values from the argument vector to this one.
    `Matrix` | DelegatingVector.`cross(Vector vec)` Gets the cross product of this vector and the other vector.
    `double` | DelegatingVector.`dot(Vector vec)` Gets dot product of two vectors.
    `<T> T` | DelegatingVector.`foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal)` Combines & maps two vector and folds them into a single value.
    `double` | DelegatingVector.`getDistanceSquared(Vector vec)` Get the square of the distance between this vector and the argument vector.
    `Vector` | DelegatingVector.`map(Vector vec, IgniteBiFunction<Double,Double,Double> fun)` Maps all values in this vector through a given function.
    `Vector` | DelegatingVector.`minus(Vector vec)` Creates new vector containing element by element difference between this vector and the argument one.
    `Vector` | DelegatingVector.`plus(Vector vec)` Creates new vector containing element by element sum from both vectors.
    `Vector` | DelegatingVector.`times(Vector vec)` Gets a new vector that is an element-wie product of this vector and the argument.

    Constructors in org.apache.ignite.ml.math.primitives.vector.impl with parameters of type Vector
    Constructor and Description
    `DelegatingNamedVector(Vector vector, Map<String,Integer> map)` Constructs a new instance of delegating named vector.
    `DelegatingVector(Vector dlg)`
    `VectorView(Vector parent, int off, int len)`

  - ### Uses of Vector in org.apache.ignite.ml.math.stat

    Methods in org.apache.ignite.ml.math.stat that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | DistributionMixture.`componentsProbs()`
    `Vector` | DistributionMixture.`likelihood(Vector x)`
    `Vector` | MultivariateGaussianDistribution.`mean()`

    Methods in org.apache.ignite.ml.math.stat with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | DistributionMixture.`likelihood(Vector x)`
    `double` | DistributionMixture.`prob(Vector x)`
    `double` | Distribution.`prob(Vector x)`
    `double` | MultivariateGaussianDistribution.`prob(Vector x)`

    Constructors in org.apache.ignite.ml.math.stat with parameters of type Vector
    Constructor and Description
    `DistributionMixture(Vector componentProbs, List<C> distributions)` Creates an instance of DistributionMixture.
    `MultivariateGaussianDistribution(Vector mean, Matrix covariance)` Constructs an instance of MultivariateGaussianDistribution.

  - ### Uses of Vector in org.apache.ignite.ml.math.util

    Methods in org.apache.ignite.ml.math.util that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `static Vector` | MatrixUtil.`likeVector(Matrix matrix)` Create the like vector with read-only matrices support.
    `static Vector` | MatrixUtil.`likeVector(Matrix matrix, int crd)` Create the like vector with read-only matrices support.
    `static Vector` | MatrixUtil.`zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by column-by-column with specified function.
    `static Vector` | MatrixUtil.`zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by row-by-row with specified function.
    `static Vector` | MatrixUtil.`zipWith(Vector v1, Vector v2, IgniteTriFunction<Double,Double,Integer,Double> f)` Zip two vectors with given tri-function taking as third argument position in vector (i.e. apply binary function to both vector elementwise and construct vector from results).

    Methods in org.apache.ignite.ml.math.util with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static DenseVector` | MatrixUtil.`localCopyOf(Vector vec)` TODO: IGNITE-5723, rewrite in a more optimal way.
    `static Vector` | MatrixUtil.`zipWith(Vector v1, Vector v2, IgniteTriFunction<Double,Double,Integer,Double> f)` Zip two vectors with given tri-function taking as third argument position in vector (i.e. apply binary function to both vector elementwise and construct vector from results).

    Method parameters in org.apache.ignite.ml.math.util with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static DenseMatrix` | MatrixUtil.`fromList(List<Vector> vecs, boolean entriesAreRows)`
    `static Vector` | MatrixUtil.`zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by column-by-column with specified function.
    `static Vector` | MatrixUtil.`zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by column-by-column with specified function.
    `static Vector` | MatrixUtil.`zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by row-by-row with specified function.
    `static Vector` | MatrixUtil.`zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun)` Zips two matrices by row-by-row with specified function.

  - ### Uses of Vector in org.apache.ignite.ml.multiclass

    Classes in org.apache.ignite.ml.multiclass with type parameters of type Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MultiClassModel<M extends IgniteModel<Vector,Double>>` Base class for multi-classification model for set of classifiers.
    `class` | `OneVsRestTrainer<M extends IgniteModel<Vector,Double>>` This is a common heuristic trainer for multi-class labeled models.

    Methods in org.apache.ignite.ml.multiclass with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MultiClassModel.`predict(Vector input)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.naivebayes.compound

    Methods in org.apache.ignite.ml.naivebayes.compound with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | CompoundNaiveBayesModel.`predict(Vector vector)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.naivebayes.discrete

    Methods in org.apache.ignite.ml.naivebayes.discrete with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | DiscreteNaiveBayesModel.`predict(Vector vector)`
    `double[]` | DiscreteNaiveBayesModel.`probabilityPowers(Vector vector)` Returns an array where the index correapons a label, and value corresponds `log(probability)` to be this label.

  - ### Uses of Vector in org.apache.ignite.ml.naivebayes.gaussian

    Methods in org.apache.ignite.ml.naivebayes.gaussian with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | GaussianNaiveBayesModel.`predict(Vector vector)` Returns a number of class to which the input belongs.
    `double[]` | GaussianNaiveBayesModel.`probabilityPowers(Vector vector)` Returns an array where the index correapons a label, and value corresponds `log(probability)` to be this label.

  - ### Uses of Vector in org.apache.ignite.ml.nn

    Fields in org.apache.ignite.ml.nn declared as Vector
    Modifier and Type | Field and Description
    --- | ---
    `protected Vector` | MLPLayer.`biases` Biases vector.

    Methods in org.apache.ignite.ml.nn that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | MultilayerPerceptron.`biases(int layerIdx)` Get biases of layer with given index.
    `Vector` | MultilayerPerceptron.`differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch)` Compose function in the following way: feed output of this model as input to second argument to loss function.
    `Vector` | MultilayerPerceptron.`parameters()` Get parameters vector.

    Methods in org.apache.ignite.ml.nn that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | MLPTrainer.`getLoss()` Get the loss function to be minimized during the training.

    Methods in org.apache.ignite.ml.nn with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `MultilayerPerceptron` | MultilayerPerceptron.`setBiases(int layerIdx, Vector bias)` Sets the biases of layer with a given index.
    `MultilayerPerceptron` | MultilayerPerceptron.`setParameters(Vector vector)` Set parameters.

    Method parameters in org.apache.ignite.ml.nn with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | MultilayerPerceptron.`differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch)` Compose function in the following way: feed output of this model as input to second argument to loss function.
    `MLPTrainer<P>` | MLPTrainer.`withLoss(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)` Set up the loss function to be minimized during the training.

    Constructors in org.apache.ignite.ml.nn with parameters of type Vector
    Constructor and Description
    `MLPLayer(Matrix weights, Vector biases)` Construct MLPLayer from weights and biases.

    Constructor parameters in org.apache.ignite.ml.nn with type arguments of type Vector
    Constructor and Description
    `MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed)` Constructs a new instance of multilayer perceptron trainer.
    `MLPTrainer(MLPArchitecture arch, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy, int maxIterations, int batchSize, int locIterations, long seed)` Constructs a new instance of multilayer perceptron trainer.

  - ### Uses of Vector in org.apache.ignite.ml.nn.initializers

    Methods in org.apache.ignite.ml.nn.initializers with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `void` | RandomInitializer.`initBiases(Vector biases)` In-place change values of vector representing vectors.
    `void` | MLPInitializer.`initBiases(Vector biases)` In-place change values of vector representing vectors.

  - ### Uses of Vector in org.apache.ignite.ml.optimization

    Fields in org.apache.ignite.ml.optimization with type parameters of type Vector
    Modifier and Type | Field and Description
    --- | ---
    `static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | LossFunctions.`HINGE` Hinge loss function.
    `static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | LossFunctions.`L1` L1 loss function.
    `static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | LossFunctions.`L2` L2 loss function.
    `static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | LossFunctions.`LOG` Log loss function.
    `static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | LossFunctions.`MSE` Mean squared error loss function.

    Methods in org.apache.ignite.ml.optimization that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | SmoothParametrized.`differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch)` Compose function in the following way: feed output of this model as input to second argument to loss function.

    Method parameters in org.apache.ignite.ml.optimization with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | SmoothParametrized.`differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss, Matrix inputsBatch, Matrix truthBatch)` Compose function in the following way: feed output of this model as input to second argument to loss function.

  - ### Uses of Vector in org.apache.ignite.ml.optimization.updatecalculators

    Fields in org.apache.ignite.ml.optimization.updatecalculators declared as Vector
    Modifier and Type | Field and Description
    --- | ---
    `protected Vector` | RPropParameterUpdate.`deltas` Previous iteration parameters deltas.
    `protected Vector` | RPropParameterUpdate.`prevIterationGradient` Previous iteration model partial derivatives by parameters.
    `protected Vector` | RPropParameterUpdate.`prevIterationUpdates` Previous iteration parameters updates.
    `protected Vector` | NesterovParameterUpdate.`prevIterationUpdates` Previous step weights updates.
    `protected Vector` | RPropParameterUpdate.`updatesMask` Updates mask (values by which updateCache is multiplied).

    Fields in org.apache.ignite.ml.optimization.updatecalculators with type parameters of type Vector
    Modifier and Type | Field and Description
    --- | ---
    `protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | RPropUpdateCalculator.`loss` Loss function.
    `protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>` | SimpleGDUpdateCalculator.`loss` Loss function.

    Methods in org.apache.ignite.ml.optimization.updatecalculators that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | SimpleGDParameterUpdate.`gradient()` Get gradient.
    `Vector` | NesterovParameterUpdate.`prevIterationUpdates()` Get previous step parameters updates.
    `Vector` | RPropParameterUpdate.`updatesMask()` Get updates mask (values by which updateCache is multiplied).

    Methods in org.apache.ignite.ml.optimization.updatecalculators with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `RPropParameterUpdate` | RPropParameterUpdate.`setDeltas(Vector deltas)` Set previous iteration deltas.
    `NesterovParameterUpdate` | NesterovParameterUpdate.`setPreviousUpdates(Vector updates)` Set previous step parameters updates.
    `RPropParameterUpdate` | RPropParameterUpdate.`setUpdatesMask(Vector updatesMask)` Set updates mask (values by which updateCache is multiplied).

    Method parameters in org.apache.ignite.ml.optimization.updatecalculators with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `NesterovParameterUpdate` | NesterovUpdateCalculator.`init(M mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)` Initializes the update calculator.
    `P` | ParameterUpdateCalculator.`init(M mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)` Initializes the update calculator.
    `RPropParameterUpdate` | RPropUpdateCalculator.`init(SmoothParametrized mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)` Initializes the update calculator.
    `SimpleGDParameterUpdate` | SimpleGDUpdateCalculator.`init(SmoothParametrized mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)` Initializes the update calculator.

    Constructors in org.apache.ignite.ml.optimization.updatecalculators with parameters of type Vector
    Constructor and Description
    `NesterovParameterUpdate(Vector prevIterationUpdates)` Construct NesterovParameterUpdate.
    `RPropParameterUpdate(Vector prevIterationUpdates, Vector prevIterationGradient, Vector deltas, Vector updatesMask)` Construct instance of this class by given parameters.
    `SimpleGDParameterUpdate(Vector gradient)` Construct instance of this class.

  - ### Uses of Vector in org.apache.ignite.ml.pipeline

    Methods in org.apache.ignite.ml.pipeline that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `IgniteModel<Vector,Double>` | PipelineMdl.`getInternalMdl()`

    Methods in org.apache.ignite.ml.pipeline with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | PipelineMdl.`predict(Vector vector)`

    Method parameters in org.apache.ignite.ml.pipeline with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `PipelineMdl<K,V>` | PipelineMdl.`withInternalMdl(IgniteModel<Vector,Double> internalMdl)`

  - ### Uses of Vector in org.apache.ignite.ml.preprocessing.imputing

    Constructors in org.apache.ignite.ml.preprocessing.imputing with parameters of type Vector
    Constructor and Description
    `ImputerPreprocessor(Vector imputingValues, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of imputing preprocessor.

  - ### Uses of Vector in org.apache.ignite.ml.recommendation

    Methods in org.apache.ignite.ml.recommendation that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Map<O,Vector>` | RecommendationModel.`getObjMatrix()`
    `Map<S,Vector>` | RecommendationModel.`getSubjMatrix()`

    Constructor parameters in org.apache.ignite.ml.recommendation with type arguments of type Vector
    Constructor and Description
    `RecommendationModel(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix)` Constructs a new instance of recommendation model.
    `RecommendationModel(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix)` Constructs a new instance of recommendation model.

  - ### Uses of Vector in org.apache.ignite.ml.recommendation.util

    Methods in org.apache.ignite.ml.recommendation.util that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Map<O,Vector>` | MatrixFactorizationGradient.`getObjGrad()` Returns gradient of object of recommendation matrix (unmodifiable).
    `Map<S,Vector>` | MatrixFactorizationGradient.`getSubjGrad()` Returns gradient of subject of recommendation function (unmodifiable).

    Method parameters in org.apache.ignite.ml.recommendation.util with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `void` | MatrixFactorizationGradient.`applyGradient(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix)` Applies given gradient to recommendation model (object matrix and subject matrix) and updates this model correspondingly.
    `void` | MatrixFactorizationGradient.`applyGradient(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix)` Applies given gradient to recommendation model (object matrix and subject matrix) and updates this model correspondingly.
    `MatrixFactorizationGradient<O,S>` | RecommendationDatasetData.`calculateGradient(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix, int batchSize, int seed, double regParam, double learningRate)` Calculates gradient of the loss function of recommendation system SGD training.
    `MatrixFactorizationGradient<O,S>` | RecommendationDatasetData.`calculateGradient(Map<O,Vector> objMatrix, Map<S,Vector> subjMatrix, int batchSize, int seed, double regParam, double learningRate)` Calculates gradient of the loss function of recommendation system SGD training.

    Constructor parameters in org.apache.ignite.ml.recommendation.util with type arguments of type Vector
    Constructor and Description
    `MatrixFactorizationGradient(Map<O,Vector> objGrad, Map<S,Vector> subjGrad, int rows)` Constructs a new instance of matrix factorization gradient.
    `MatrixFactorizationGradient(Map<O,Vector> objGrad, Map<S,Vector> subjGrad, int rows)` Constructs a new instance of matrix factorization gradient.

  - ### Uses of Vector in org.apache.ignite.ml.regressions.linear

    Methods in org.apache.ignite.ml.regressions.linear that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | LinearRegressionModel.`weights()`

    Methods in org.apache.ignite.ml.regressions.linear with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | LinearRegressionModel.`predict(Vector input)` Make a prediction for the specified input arguments.
    `LinearRegressionModel` | LinearRegressionModel.`withWeights(Vector weights)` Set up the weights.

    Constructors in org.apache.ignite.ml.regressions.linear with parameters of type Vector
    Constructor and Description
    `LinearRegressionModel(Vector weights, double intercept)`

  - ### Uses of Vector in org.apache.ignite.ml.regressions.logistic

    Methods in org.apache.ignite.ml.regressions.logistic that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | LogisticRegressionModel.`weights()` Gets the weights.

    Methods in org.apache.ignite.ml.regressions.logistic with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | LogisticRegressionModel.`predict(Vector input)` Make a prediction for the specified input arguments.
    `LogisticRegressionModel` | LogisticRegressionModel.`withWeights(Vector weights)` Set up the weights.

    Constructors in org.apache.ignite.ml.regressions.logistic with parameters of type Vector
    Constructor and Description
    `LogisticRegressionModel(Vector weights, double intercept)`

  - ### Uses of Vector in org.apache.ignite.ml.selection.cv

    Classes in org.apache.ignite.ml.selection.cv with type parameters of type Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `AbstractCrossValidation<M extends IgniteModel<Vector,Double>,K,V>` Cross validation score calculator.
    `class` | `CrossValidation<M extends IgniteModel<Vector,Double>,K,V>` Cross validation score calculator.
    `class` | `DebugCrossValidation<M extends IgniteModel<Vector,Double>,K,V>` Cross validation score calculator.

  - ### Uses of Vector in org.apache.ignite.ml.selection.scoring.cursor

    Constructor parameters in org.apache.ignite.ml.selection.scoring.cursor with type arguments of type Vector
    Constructor and Description
    `CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of cache based truth with prediction cursor.
    `CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of cache based truth with prediction cursor.
    `LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of local truth with prediction cursor.

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

    Method parameters in org.apache.ignite.ml.selection.scoring.evaluator with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V> EvaluationResult` | Evaluator.`evaluate(DatasetBuilder<K,V> datasetBuilder, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric... metrics)` Evaluate model.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).

  - ### Uses of Vector in org.apache.ignite.ml.selection.scoring.evaluator.aggregator

    Method parameters in org.apache.ignite.ml.selection.scoring.evaluator.aggregator with type arguments of type Vector
    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 Vector in org.apache.ignite.ml.structures

    Classes in org.apache.ignite.ml.structures with type parameters of type Vector
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DatasetRow<V extends Vector>` Class to keep one observation in dataset.

    Fields in org.apache.ignite.ml.structures declared as Vector
    Modifier and Type | Field and Description
    --- | ---
    `protected V` | DatasetRow.`vector` Vector.

    Methods in org.apache.ignite.ml.structures that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `static Vector` | LabeledVectorSet.`emptyVector(int size)`
    `Vector` | Dataset.`features(int idx)` Get the features.

    Constructors in org.apache.ignite.ml.structures with parameters of type Vector
    Constructor and Description
    `LabeledVector(Vector vector, L lb)` Construct labeled vector.

  - ### Uses of Vector in org.apache.ignite.ml.svm

    Methods in org.apache.ignite.ml.svm that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | SVMLinearClassificationModel.`weights()` Gets the weights.

    Methods in org.apache.ignite.ml.svm with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | SVMLinearClassificationModel.`predict(Vector input)` Make a prediction for the specified input arguments.
    `SVMLinearClassificationModel` | SVMLinearClassificationModel.`withWeights(Vector weights)` Set up the weights.

    Constructors in org.apache.ignite.ml.svm with parameters of type Vector
    Constructor and Description
    `SVMLinearClassificationModel(Vector weights, double intercept)`

  - ### Uses of Vector in org.apache.ignite.ml.trainers

    Methods in org.apache.ignite.ml.trainers with type parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static <M extends IgniteModel<Vector,Double>,L> BaggedTrainer<L>` | TrainerTransformers.`makeBagged(DatasetTrainer<M,L> trainer, int ensembleSize, double subsampleRatio, int featureVectorSize, int featuresSubspaceDim, PredictionsAggregator aggregator)` Add bagging logic to a given trainer.

    Methods in org.apache.ignite.ml.trainers that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `default Vector` | FeatureLabelExtractor.`extractFeatures(K key, V val)` Extract features from key and value.

    Method parameters in org.apache.ignite.ml.trainers with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `AdaptableDatasetTrainer<I,O,IW,OW,M,L>` | AdaptableDatasetTrainer.`afterFeatureExtractor(IgniteFunction<Vector,Vector> after)` Specify function which will be applied after feature extractor.
    `AdaptableDatasetTrainer<I,O,IW,OW,M,L>` | AdaptableDatasetTrainer.`afterFeatureExtractor(IgniteFunction<Vector,Vector> after)` Specify function which will be applied after feature extractor.

  - ### Uses of Vector in org.apache.ignite.ml.tree

    Methods in org.apache.ignite.ml.tree with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | DecisionTreeLeafNode.`predict(Vector doubles)` Make a prediction for the specified input arguments.
    `Double` | DecisionTreeConditionalNode.`predict(Vector features)` Make a prediction for the specified input arguments.
    `Double` | DecisionTreeModel.`predict(Vector features)` Make a prediction for the specified input arguments.

  - ### Uses of Vector in org.apache.ignite.ml.tree.boosting

    Methods in org.apache.ignite.ml.tree.boosting that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> List<IgniteModel<Vector,Double>>` | GDBOnTreesLearningStrategy.`update(GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer)` Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

  - ### Uses of Vector in org.apache.ignite.ml.tree.impurity

    Methods in org.apache.ignite.ml.tree.impurity that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `protected Vector` | ImpurityMeasureCalculator.`getFeatureValues(DecisionTreeData data, TreeDataIndex idx, int featureId, int k)` Returns feature value in according to kth order statistic.

  - ### Uses of Vector in org.apache.ignite.ml.tree.randomforest.data

    Methods in org.apache.ignite.ml.tree.randomforest.data with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `Double` | RandomForestTreeModel.`predict(Vector vector)` Make a prediction for the specified input arguments.
    `Double` | TreeNode.`predict(Vector features)` Make a prediction for the specified input arguments.
    `NodeId` | TreeNode.`predictNextNodeKey(Vector features)` Returns leaf node for feature vector in according to decision tree.

  - ### Uses of Vector in org.apache.ignite.ml.util.generators

    Methods in org.apache.ignite.ml.util.generators that return types with arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions)` Convert first N values from stream to `DatasetBuilder`.
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions, UpstreamTransformerBuilder upstreamTransformerBuilder)` Convert first N values from stream to `DatasetBuilder`.
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, int partitions)` Convert first N values from stream to `DatasetBuilder`.
    `default Map<Vector,Double>` | DataStreamGenerator.`asMap(int datasetSize)` Convert first N values from stream to map.
    `default Stream<Vector>` | DataStreamGenerator.`unlabeled()`

    Method parameters in org.apache.ignite.ml.util.generators with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions)` Convert first N values from stream to `DatasetBuilder`.
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions, UpstreamTransformerBuilder upstreamTransformerBuilder)` Convert first N values from stream to `DatasetBuilder`.
    `default Stream<LabeledVector<Double>>` | DataStreamGenerator.`labeled(IgniteFunction<Vector,Double> classifier)`
    `default DataStreamGenerator` | DataStreamGenerator.`mapVectors(IgniteFunction<Vector,Vector> f)` Apply user defined mapper to vectors stream without labels hiding.
    `default DataStreamGenerator` | DataStreamGenerator.`mapVectors(IgniteFunction<Vector,Vector> f)` Apply user defined mapper to vectors stream without labels hiding.

  - ### Uses of Vector in org.apache.ignite.ml.util.generators.primitives.scalar

    Methods in org.apache.ignite.ml.util.generators.primitives.scalar that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `default Vector` | RandomProducer.`noizify(Vector vector)` Adds values generated by random producer to each vector value.

    Methods in org.apache.ignite.ml.util.generators.primitives.scalar with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `default Vector` | RandomProducer.`noizify(Vector vector)` Adds values generated by random producer to each vector value.

  - ### Uses of Vector in org.apache.ignite.ml.util.generators.primitives.vector

    Methods in org.apache.ignite.ml.util.generators.primitives.vector that return Vector
    Modifier and Type | Method and Description
    --- | ---
    `Vector` | ParametricVectorGenerator.`get()`
    `Vector` | VectorGeneratorsFamily.`get()`
    `Vector` | VectorGeneratorsFamily.VectorWithDistributionId.`vector()`

    Methods in org.apache.ignite.ml.util.generators.primitives.vector with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `static VectorGenerator` | VectorGeneratorPrimitives.`constant(Vector v)`
    `static VectorGenerator` | VectorGeneratorPrimitives.`gauss(Vector means, Vector variances)` Returns vector generator of vectors from multidimensional gauss distribution.
    `static VectorGenerator` | VectorGeneratorPrimitives.`gauss(Vector means, Vector variances, Long seed)` Returns vector generator of vectors from multidimensional gauss distribution.
    `default VectorGenerator` | VectorGenerator.`move(Vector v)` Moves all vectors to other position by summing with input vector.
    `static VectorGenerator` | VectorGeneratorPrimitives.`parallelogram(Vector bounds)` Returns vector generator of vectors from multidimensional uniform distribution around zero.
    `static VectorGenerator` | VectorGeneratorPrimitives.`parallelogram(Vector bounds, long seed)` Returns vector generator of vectors from multidimensional uniform distribution around zero.

    Method parameters in org.apache.ignite.ml.util.generators.primitives.vector with type arguments of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `default VectorGenerator` | VectorGenerator.`filter(IgnitePredicate<Vector> predicate)` Filters values of vector generator using predicate.
    `default VectorGenerator` | VectorGenerator.`map(IgniteFunction<Vector,Vector> mapper)` Maps values of vector generator using mapper.
    `default VectorGenerator` | VectorGenerator.`map(IgniteFunction<Vector,Vector> mapper)` Maps values of vector generator using mapper.

    Constructors in org.apache.ignite.ml.util.generators.primitives.vector with parameters of type Vector
    Constructor and Description
    `VectorWithDistributionId(Vector vector, int distributionId)` Creates an instance of VectorWithDistributionId.

  - ### Uses of Vector in org.apache.ignite.ml.util.generators.standard

    Methods in org.apache.ignite.ml.util.generators.standard with parameters of type Vector
    Modifier and Type | Method and Description
    --- | ---
    `GaussianMixtureDataStream.Builder` | GaussianMixtureDataStream.Builder.`add(Vector mean, Vector variance)` Adds multidimensional gaussian component.

    Constructor parameters in org.apache.ignite.ml.util.generators.standard with type arguments of type Vector
    Constructor and Description
    `RegressionDataStream(int vectorSize, IgniteFunction<Vector,Double> function, double minXVal, double maxXVal)` Creates an instance of RegressionDataStream.

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