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

Packages that use Vector

Package	Description
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.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 convergency checking algorithms for gradient boosting.
org.apache.ignite.ml.composition.boosting.convergence.mean	Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
org.apache.ignite.ml.composition.boosting.convergence.median	Contains implementation of convergence checking computer by median of medians of errors in dataset.
org.apache.ignite.ml.composition.boosting.convergence.simple	Contains implementation of Stub for convergence checking.
org.apache.ignite.ml.dataset	Base package for machine learning dataset classes.
org.apache.ignite.ml.dataset.impl.bootstrapping	Base package for bootstrapped implementation of machine learning dataset.
org.apache.ignite.ml.dataset.primitive	Package that contains basic primitives build on top of Dataset.
org.apache.ignite.ml.dataset.primitive.builder.data	Contains partition data builders.
org.apache.ignite.ml.environment.logging	Package contains several logging strategy realisations.
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.math	Contains main APIs for matrix/vector algebra.
org.apache.ignite.ml.math.distances	Contains main APIs for distances.
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.util	Some math utils.
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.binarization	Contains binarization preprocessor.
org.apache.ignite.ml.preprocessing.encoding.onehotencoder	Contains one hot encoding preprocessor.
org.apache.ignite.ml.preprocessing.encoding.stringencoder	Contains string encoding preprocessor.
org.apache.ignite.ml.preprocessing.imputing	Contains Imputer preprocessor.
org.apache.ignite.ml.preprocessing.maxabsscaling	Contains Max Abs Scaler preprocessor.
org.apache.ignite.ml.preprocessing.minmaxscaling	Contains Min Max Scaler preprocessor.
org.apache.ignite.ml.preprocessing.normalization	Contains Normalizer preprocessor.
org.apache.ignite.ml.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.regressions.logistic.binomial	Contains binomial logistic regression.
org.apache.ignite.ml.regressions.logistic.multiclass	Contains multi-class logistic regression.
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
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.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.data	Contains data and data builder required for decision tree trainers built on top of partition based dataset.
org.apache.ignite.ml.tree.impurity	Root package for decision tree impurity measures and calculators.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.
org.apache.ignite.ml.tree.randomforest.data	Package contains helper data structures for random forest implementation.

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[]	KMeansModelFormat.getCenters()
Vector[]	KMeansModel.getCenters() Get centers of clusters.

Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type Vector

Modifier and Type	Method and Description
Integer	KMeansModel.apply(Vector vec) Predict closest center index for a given vector.

Method parameters in org.apache.ignite.ml.clustering.kmeans with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> KMeansModel	KMeansTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> KMeansModel	KMeansTrainer.updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

Methods in org.apache.ignite.ml.composition that return types with arguments of type Vector

Modifier and Type	Method and Description
protected abstract DatasetTrainer<? extends Model<Vector,Double>,Double>	BaggingModelTrainer.buildDatasetTrainerForModel() Creates trainer specific to ensemble.
Model<Vector,Double>	ModelOnFeaturesSubspace.getMdl() Returns model.
List<Model<Vector,Double>>	ModelsComposition.getModels() Returns containing models.
List<Model<Vector,Double>>	ModelsCompositionFormat.models()

Methods in org.apache.ignite.ml.composition with parameters of type Vector

Modifier and Type	Method and Description
Double	ModelsComposition.apply(Vector features) Applies containing models to features and aggregate them to one prediction.
Double	ModelOnFeaturesSubspace.apply(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
<K,V> ModelsComposition	BaggingModelTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	BaggingModelTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Learn new models on dataset and create new Compositions over them and already learned models.

Constructor parameters in org.apache.ignite.ml.composition with type arguments of type Vector

Constructor and Description
ModelsComposition(List<? extends Model<Vector,Double>> models, PredictionsAggregator predictionsAggregator) Constructs a new instance of composition of models.
ModelsCompositionFormat(List<Model<Vector,Double>> models, PredictionsAggregator predictionsAggregator) Creates an instance of ModelsCompositionFormat.

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 Model<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
protected abstract DatasetTrainer<? extends Model<Vector,Double>,Double>	GDBTrainer.buildBaseModelTrainer() Returns regressor model trainer for one step of GDB.
protected List<Model<Vector,Double>>	GDBLearningStrategy.initLearningState(GDBTrainer.GDBModel mdlToUpdate) Restores state of already learned model if can and sets learning parameters according to this state.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Implementation of gradient boosting iterations.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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	GDBTrainer.GDBModel.apply(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
protected <V,K> IgniteBiTuple<Double,Long>	GDBTrainer.computeInitialValue(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Compute mean value of label as first approximation.
<K,V> ModelsComposition	GDBTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <V,K> boolean	GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected abstract <V,K> boolean	GDBTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Implementation of gradient boosting iterations.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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.
protected <K,V> ModelsComposition	GDBTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
GDBLearningStrategy	GDBLearningStrategy.withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends Model<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 Model<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.

Method parameters in org.apache.ignite.ml.composition.boosting.convergence with type arguments of type Vector

Modifier and Type	Method and Description
abstract <K,V> ConvergenceChecker<K,V>	ConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructor parameters in org.apache.ignite.ml.composition.boosting.convergence with type arguments of type Vector

Constructor and Description
ConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Constructs an instance of ConvergenceChecker.

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

Method parameters in org.apache.ignite.ml.composition.boosting.convergence.mean with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MeanAbsValueConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructor parameters in org.apache.ignite.ml.composition.boosting.convergence.mean with type arguments of type Vector

Constructor and Description
MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Creates an intance of MeanAbsValueConvergenceChecker.

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

Method parameters in org.apache.ignite.ml.composition.boosting.convergence.median with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MedianOfMedianConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructor parameters in org.apache.ignite.ml.composition.boosting.convergence.median with type arguments of type Vector

Constructor and Description
MedianOfMedianConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> lblMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> fExtr, IgniteBiFunction<K,V,Double> lbExtr, double precision) Creates an instance of MedianOfMedianConvergenceChecker.

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

Method parameters in org.apache.ignite.ml.composition.boosting.convergence.simple with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	ConvergenceCheckerStubFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructor parameters in org.apache.ignite.ml.composition.boosting.convergence.simple with type arguments of type Vector

Constructor and Description
ConvergenceCheckerStub(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Creates an intance of ConvergenceCheckerStub.

Uses of Vector in org.apache.ignite.ml.dataset

Method parameters in org.apache.ignite.ml.dataset with type arguments of type Vector

Modifier and Type	Method and Description
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of local SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of local SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of local SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of local SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

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.

Constructor parameters in org.apache.ignite.ml.dataset.impl.bootstrapping with type arguments of type Vector

Constructor and Description
BootstrappedDatasetBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, int samplesCnt, double subsampleSize) Creates an instance of BootstrappedDatasetBuilder.

Uses of Vector in org.apache.ignite.ml.dataset.primitive

Constructor parameters in org.apache.ignite.ml.dataset.primitive with type arguments of type Vector

Constructor and Description
FeatureMatrixWithLabelsOnHeapDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Constructs a new instance of decision tree data builder.

Uses of Vector in org.apache.ignite.ml.dataset.primitive.builder.data

Constructor parameters in org.apache.ignite.ml.dataset.primitive.builder.data with type arguments of type Vector

Constructor and Description
SimpleDatasetDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor) Construct a new instance of partition data builder that makes SimpleDatasetData.
SimpleLabeledDatasetDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Constructs a new instance of partition data builder that makes SimpleLabeledDatasetData.

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	ConsoleLogger.log(Vector vector) Log vector.
Vector	CustomMLLogger.log(Vector vector) Log vector.
Vector	NoOpLogger.log(Vector vector) Log vector.
Vector	MLLogger.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	ConsoleLogger.log(Vector vector) Log vector.
Vector	CustomMLLogger.log(Vector vector) Log vector.
Vector	NoOpLogger.log(Vector vector) Log vector.
Vector	MLLogger.log(Vector vector) Log vector.

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
protected TreeMap<Double,Set<Integer>>	NNClassificationModel.getDistances(Vector v, LabeledVectorSet<Double,LabeledVector> trainingData) Computes distances between given vector and each vector in training dataset.

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

Modifier and Type	Method and Description
static <K,V> Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>	KNNUtils.buildDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Builds 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.apply(Vector v)

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

Modifier and Type	Method and Description
<K,V> ANNClassificationModel	ANNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ANNClassificationModel	ANNClassificationTrainer.updateModel(ANNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
<K,V> KNNClassificationModel	KNNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNClassificationModel	KNNClassificationTrainer.updateModel(KNNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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.apply(Vector v)

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

Modifier and Type	Method and Description
<K,V> KNNRegressionModel	KNNRegressionTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNRegressionModel	KNNRegressionTrainer.updateModel(KNNRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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	HammingDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	ManhattanDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	EuclideanDistance.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	DistanceMeasure.compute(Vector a, double[] b) Compute the distance between n-dimensional vector and n-dimensional array.
double	HammingDistance.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	EuclideanDistance.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.

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	AbstractMatrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	Matrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns 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.foldRows(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all rows in this matrix.
Vector	AbstractMatrix.getCol(int col) Get a specific row from matrix.
Vector	Matrix.getCol(int col) Get a specific row from matrix.
Vector	AbstractMatrix.getRow(int row) Get a specific row from matrix.
Vector	Matrix.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	AbstractMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	Matrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	AbstractMatrix.viewColumn(int col) Creates new view into matrix column .
Vector	Matrix.viewColumn(int col) Creates new view into matrix column .
Vector	AbstractMatrix.viewDiagonal() Creates new view into matrix diagonal.
Vector	Matrix.viewDiagonal() Creates new view into matrix diagonal.
Vector	AbstractMatrix.viewRow(int row) Creates new view into matrix row.
Vector	Matrix.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	AbstractMatrix.assignColumn(int col, Vector vec) Assigns values from given vector to the specified column in this matrix.
Matrix	Matrix.assignColumn(int col, Vector vec) Assigns values from given vector to the specified column in this matrix.
Matrix	AbstractMatrix.assignRow(int row, Vector vec) Assigns values from given vector to the specified row in this matrix.
Matrix	Matrix.assignRow(int row, Vector vec) Assigns values from given vector to the specified row in this matrix.
Vector	AbstractMatrix.times(Vector vec) Creates new matrix that is the product of multiplying this matrix and the argument vector.
Vector	Matrix.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	AbstractMatrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns in this matrix.
Vector	Matrix.foldColumns(IgniteFunction<Vector,Double> fun) Collects the results of applying a given function to all columns 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.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	ViewMatrix.likeVector(int crd) Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.
Vector	SparseMatrix.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

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.
Vector	Vector.copy() Creates new copy of this vector.
Vector	AbstractVector.copy() Creates new copy of this 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(int num, int vecSize) Turn number into a local Vector of given size with one-hot encoding.
static Vector	VectorUtils.num2Vec(int num, int vecSize, boolean isDistributed) Turn number into Vector of given size with one-hot encoding.
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.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 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.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	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	VectorView.copy() Creates new copy of this vector.
Vector	VectorizedViewMatrix.copy() Creates new copy of this vector.
Vector	DelegatingVector.copy() Creates new copy of this 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	VectorView.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	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	DelegatingVector.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.setX(int idx, double val) Sets value without checking for index boundaries.
Vector	DelegatingVector.sort() Sorts this vector in ascending order.
Vector	SparseVector.times(double x) Gets a new vector that contains product of each element and the argument.
Vector	DelegatingVector.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
DelegatingVector(Vector dlg)
VectorView(Vector parent, int off, int len)

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.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.
protected Vector	MultilayerPerceptron.paramsAsVector(List<MLPLayer> layersParams) Flatten this MLP parameters as 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.
<K,V> MultilayerPerceptron	MLPTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Trains model based on the specified data.
protected <K,V> MultilayerPerceptron	MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
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	NesterovParameterUpdate.prevIterationUpdates Previous step weights updates.
protected Vector	RPropParameterUpdate.prevIterationUpdates Previous iteration parameters 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>	SimpleGDUpdateCalculator.loss Loss function.
protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction>	RPropUpdateCalculator.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.
SimpleGDParameterUpdate	SimpleGDUpdateCalculator.init(SmoothParametrized mdl, IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss) Initializes the update calculator.
RPropParameterUpdate	RPropUpdateCalculator.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
IgniteBiFunction<K,V,Vector>	PipelineMdl.getFeatureExtractor()
Model<Vector,Double>	PipelineMdl.getInternalMdl()

Methods in org.apache.ignite.ml.pipeline with parameters of type Vector

Modifier and Type	Method and Description
Double	PipelineMdl.apply(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(Model<Vector,Double> internalMdl)

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

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

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

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

Modifier and Type	Method and Description
BinarizationPreprocessor<K,V>	BinarizationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Constructor parameters in org.apache.ignite.ml.preprocessing.binarization with type arguments of type Vector

Constructor and Description
BinarizationPreprocessor(double threshold, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of Binarization preprocessor.

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
ImputerPreprocessor<K,V>	ImputerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Constructors in org.apache.ignite.ml.preprocessing.imputing with parameters of type Vector

Constructor and Description
ImputerPreprocessor(Vector imputingValues, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of imputing preprocessor.

Constructor parameters in org.apache.ignite.ml.preprocessing.imputing with type arguments of type Vector

Constructor and Description
ImputerPreprocessor(Vector imputingValues, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of imputing preprocessor.

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

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

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

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

Modifier and Type	Method and Description
MaxAbsScalerPreprocessor<K,V>	MaxAbsScalerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Constructor parameters in org.apache.ignite.ml.preprocessing.maxabsscaling with type arguments of type Vector

Constructor and Description
MaxAbsScalerPreprocessor(double[] maxAbs, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of maxabsscaling preprocessor.

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

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

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

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

Modifier and Type	Method and Description
MinMaxScalerPreprocessor<K,V>	MinMaxScalerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Constructor parameters in org.apache.ignite.ml.preprocessing.minmaxscaling with type arguments of type Vector

Constructor and Description
MinMaxScalerPreprocessor(double[] min, double[] max, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of minmaxscaling preprocessor.

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

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

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

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

Modifier and Type	Method and Description
NormalizationPreprocessor<K,V>	NormalizationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Constructor parameters in org.apache.ignite.ml.preprocessing.normalization with type arguments of type Vector

Constructor and Description
NormalizationPreprocessor(int p, IgniteBiFunction<K,V,Vector> basePreprocessor) Constructs a new instance of Normalization preprocessor.

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	FeatureExtractorWrapper.apply(K k, V v)
Vector	LinearRegressionModel.getWeights()

Methods in org.apache.ignite.ml.regressions.linear with parameters of type Vector

Modifier and Type	Method and Description
Double	LinearRegressionModel.apply(Vector input)

Method parameters in org.apache.ignite.ml.regressions.linear with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> LinearRegressionModel	LinearRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> LinearRegressionModel	LinearRegressionSGDTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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.binomial

Methods in org.apache.ignite.ml.regressions.logistic.binomial that return Vector

Modifier and Type	Method and Description
Vector	LogisticRegressionModel.weights() Gets the weights.

Methods in org.apache.ignite.ml.regressions.logistic.binomial with parameters of type Vector

Modifier and Type	Method and Description
Double	LogisticRegressionModel.apply(Vector input)
LogisticRegressionModel	LogisticRegressionModel.withWeights(Vector weights) Set up the weights.

Method parameters in org.apache.ignite.ml.regressions.logistic.binomial with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Constructors in org.apache.ignite.ml.regressions.logistic.binomial with parameters of type Vector

Constructor and Description
LogisticRegressionModel(Vector weights, double intercept)

Uses of Vector in org.apache.ignite.ml.regressions.logistic.multiclass

Methods in org.apache.ignite.ml.regressions.logistic.multiclass with parameters of type Vector

Modifier and Type	Method and Description
Double	LogRegressionMultiClassModel.apply(Vector input)

Method parameters in org.apache.ignite.ml.regressions.logistic.multiclass with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.updateModel(LogRegressionMultiClassModel newMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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	CrossValidation<M extends Model<Vector,L>,L,K,V> Cross validation score calculator.

Method parameters in org.apache.ignite.ml.selection.cv with type arguments of type Vector

Modifier and Type	Method and Description
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
CrossValidationResult	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv, ParamGrid paramGrid) Computes cross-validated metrics with a passed parameter grid.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.

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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<Vector,L> mdl) Constructs a new instance of local truth with prediction cursor.
LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Model<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 <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.

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.
class	LabeledVector<V extends Vector,L> Class for vector with label.

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, boolean isDistributed)
Vector	Dataset.features(int idx) Get the features.

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

Constructor parameters in org.apache.ignite.ml.structures.partition with type arguments of type Vector

Constructor and Description
LabeledDatasetPartitionDataBuilderOnHeap(IgniteBiFunction<K,V,Vector> xExtractor, IgniteBiFunction<K,V,Double> yExtractor) Constructs a new instance of SVM partition data builder.

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	SVMLinearBinaryClassificationModel.weights() Gets the weights.

Methods in org.apache.ignite.ml.svm with parameters of type Vector

Modifier and Type	Method and Description
Double	SVMLinearMultiClassClassificationModel.apply(Vector input)
Double	SVMLinearBinaryClassificationModel.apply(Vector input)
SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationModel.withWeights(Vector weights) Set up the weights.

Method parameters in org.apache.ignite.ml.svm with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.updateModel(SVMLinearBinaryClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.updateModel(SVMLinearMultiClassClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Constructors in org.apache.ignite.ml.svm with parameters of type Vector

Constructor and Description
SVMLinearBinaryClassificationModel(Vector weights, double intercept)

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

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

Modifier and Type	Method and Description
abstract <K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.update(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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.
<K,V> M	DatasetTrainer.update(M mdl, Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Map<K,V> data, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected abstract <K,V> M	DatasetTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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.apply(Vector doubles)
Double	DecisionTreeConditionalNode.apply(Vector features)

Method parameters in org.apache.ignite.ml.tree with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> DecisionTreeNode	DecisionTree.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> DecisionTreeNode	DecisionTree.updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains new model based on dataset because there is no valid approach to update decision trees.

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<Model<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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.

Method parameters in org.apache.ignite.ml.tree.boosting with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> List<Model<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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.data

Constructor parameters in org.apache.ignite.ml.tree.data with type arguments of type Vector

Constructor and Description
DecisionTreeDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, boolean buildIdx) Constructs a new instance of decision tree data builder.

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)

Uses of Vector in org.apache.ignite.ml.tree.randomforest

Method parameters in org.apache.ignite.ml.tree.randomforest with type arguments of type Vector

Modifier and Type	Method and Description
<K,V> ModelsComposition	RandomForestTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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	TreeRoot.apply(Vector vector)
Double	TreeNode.apply(Vector features)
NodeId	TreeNode.predictNextNodeKey(Vector features) Returns leaf node for feature vector in according to decision tree.