## Uses of Class
org.apache.ignite.ml.dataset.primitive.context.EmptyContext

- Packages that use EmptyContext
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.boosting.convergence | Package contains implementation of convergence 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.builder.context | Contains partition `context` builders.
  org.apache.ignite.ml.knn | Contains main APIs for kNN 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.nn | Contains neural networks and related classes.
  org.apache.ignite.ml.recommendation.util | Contains util classes used in recommendation system framework.
  org.apache.ignite.ml.tree | Root package for decision trees.
  org.apache.ignite.ml.tree.leaf | Root package for decision trees leaf builders.
  org.apache.ignite.ml.tree.randomforest | Contains random forest implementation classes.
  org.apache.ignite.ml.tree.randomforest.data.impurity | Contains implementation of impurity computers based on histograms.
  org.apache.ignite.ml.tree.randomforest.data.statistics | Contains implementation of statistics computers for Random Forest.

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

    Method parameters in org.apache.ignite.ml.composition.boosting.convergence with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `abstract Double` | ConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceChecker.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.

  - ### Uses of EmptyContext 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 EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MeanAbsValueConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of EmptyContext 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 EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MedianOfMedianConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of EmptyContext 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 EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `Double` | ConvergenceCheckerStub.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceCheckerStub.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.

  - ### Uses of EmptyContext in org.apache.ignite.ml.dataset

    Methods in org.apache.ignite.ml.dataset that return types with arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of local `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>` | DatasetFactory.`createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> vectorizer)` Creates a new instance of distributed `SimpleLabeledDataset` using the specified `featureExtractor` and `lbExtractor`.
    `static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>` | DatasetFactory.`createSimpleLabeledDataset(Ignite ignite, LearningEnvironmentBuilder envBuilder, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> vectorizer)` Creates a new instance of distributed `SimpleLabeledDataset` using the specified `featureExtractor` and `lbExtractor`.
    `static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>` | DatasetFactory.`createSimpleLabeledDataset(Map<K,V> upstreamMap, LearningEnvironmentBuilder envBuilder, int partitions, Preprocessor<K,V> vectorizer)` Creates a new instance of local `SimpleLabeledDataset` using the specified `featureExtractor` and `lbExtractor`.

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

    Methods in org.apache.ignite.ml.dataset.impl.bootstrapping with parameters of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `BootstrappedDatasetPartition` | BootstrappedDatasetBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, EmptyContext ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

  - ### Uses of EmptyContext in org.apache.ignite.ml.dataset.primitive.builder.context

    Methods in org.apache.ignite.ml.dataset.primitive.builder.context that return EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `EmptyContext` | EmptyContextBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize)` Builds a new partition `context` from an `upstream` data.

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

    Methods in org.apache.ignite.ml.knn with parameters of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `SpatialIndex<Double>` | KNNPartitionDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, EmptyContext ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

    Method parameters in org.apache.ignite.ml.knn with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `protected abstract M` | KNNTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

    Constructor parameters in org.apache.ignite.ml.knn with type arguments of type EmptyContext
    Constructor and Description
    `KNNModel(Dataset<EmptyContext,SpatialIndex<L>> dataset, DistanceMeasure distanceMeasure, int k, boolean weighted)` Constructs a new instance of KNN model.

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

    Method parameters in org.apache.ignite.ml.knn.classification with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `protected KNNClassificationModel` | KNNClassificationTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

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

    Method parameters in org.apache.ignite.ml.knn.regression with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `protected KNNRegressionModel` | KNNRegressionTrainer.`convertDatasetIntoModel(Dataset<EmptyContext,SpatialIndex<Double>> dataset)` Convers given dataset into KNN model (classification or regression depends on implementation).

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

    Methods in org.apache.ignite.ml.nn that return types with arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture>` | MLPTrainer.`getArchSupplier()` Get the multilayer perceptron architecture supplier that defines layers and activators.

    Method parameters in org.apache.ignite.ml.nn with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `MLPTrainer<P>` | MLPTrainer.`withArchSupplier(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier)` Set up the multilayer perceptron architecture supplier that defines layers and activators.

    Constructor parameters in org.apache.ignite.ml.nn with type arguments of type EmptyContext
    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.

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

    Methods in org.apache.ignite.ml.recommendation.util with parameters of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `RecommendationDatasetData<O,S>` | RecommendationDatasetDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,Z>> upstreamData, long upstreamDataSize, EmptyContext ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.
    `RecommendationDatasetData<Serializable,Serializable>` | RecommendationBinaryDatasetDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<Object,BinaryObject>> upstreamData, long upstreamDataSize, EmptyContext ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

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

    Method parameters in org.apache.ignite.ml.tree with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> DecisionTreeModel` | DecisionTreeTrainer.`fit(Dataset<EmptyContext,DecisionTreeData> dataset)`
    `protected ImpurityMeasureCalculator<MSEImpurityMeasure>` | DecisionTreeRegressionTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.
    `protected abstract ImpurityMeasureCalculator<T>` | DecisionTreeTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.
    `protected ImpurityMeasureCalculator<GiniImpurityMeasure>` | DecisionTreeClassificationTrainer.`getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset)` Returns impurity measure calculator.

  - ### Uses of EmptyContext in org.apache.ignite.ml.tree.leaf

    Method parameters in org.apache.ignite.ml.tree.leaf with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `DecisionTreeLeafNode` | DecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.
    `DecisionTreeLeafNode` | MeanDecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.
    `DecisionTreeLeafNode` | MostCommonDecisionTreeLeafBuilder.`createLeafNode(Dataset<EmptyContext,DecisionTreeData> dataset, TreeFilter pred)` Creates new leaf node for given dataset and node predicate.

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

    Method parameters in org.apache.ignite.ml.tree.randomforest with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `protected boolean` | RandomForestTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Init-step before learning.
    `protected boolean` | RandomForestClassifierTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Aggregates all unique labels from dataset and assigns integer id value for each label.

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

    Method parameters in org.apache.ignite.ml.tree.randomforest.data.impurity with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `Map<NodeId,ImpurityHistogramsComputer.NodeImpurityHistograms<S>>` | ImpurityHistogramsComputer.`aggregateImpurityStatistics(ArrayList<RandomForestTreeModel> roots, Map<Integer,BucketMeta> histMeta, Map<NodeId,TreeNode> nodesToLearn, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes histograms for each feature.

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

    Method parameters in org.apache.ignite.ml.tree.randomforest.data.statistics with type arguments of type EmptyContext
    Modifier and Type | Method and Description
    --- | ---
    `List<NormalDistributionStatistics>` | NormalDistributionStatisticsComputer.`computeStatistics(List<FeatureMeta> meta, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes statistics of normal distribution on features in dataset.
    `void` | LeafValuesComputer.`setValuesForLeaves(ArrayList<RandomForestTreeModel> roots, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Takes a list of all built trees and in one map-reduceImpurityStatistics step collect statistics for evaluating leaf-values for each tree and sets values for leaves.

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Release Date | : January 10 2022