## Uses of Interface
org.apache.ignite.ml.environment.LearningEnvironment

- Packages that use LearningEnvironment
  Package | Description
  --- | ---
  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.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.impl.cache | Base package for cache based implementation of machine learning dataset.
  org.apache.ignite.ml.dataset.impl.cache.util | Contains util classes used in cache based implementation of dataset.
  org.apache.ignite.ml.dataset.impl.local | Base package for local 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.context | Contains partition `context` builders.
  org.apache.ignite.ml.dataset.primitive.builder.data | Contains partition `data` builders.
  org.apache.ignite.ml.environment | Package contains environment utils for ML algorithms.
  org.apache.ignite.ml.knn | Contains main APIs for kNN algorithms.
  org.apache.ignite.ml.math.isolve.lsqr | Contains LSQR algorithm implementation.
  org.apache.ignite.ml.preprocessing | Base package for machine learning preprocessing classes.
  org.apache.ignite.ml.recommendation | Contains recommendation system framework.
  org.apache.ignite.ml.recommendation.util | Contains util classes used in recommendation system framework.
  org.apache.ignite.ml.selection.cv | Root package for cross-validation algorithms.
  org.apache.ignite.ml.sparkmodelparser | Base package for Spark model parser.
  org.apache.ignite.ml.structures.partition | Contains internal APIs for dataset partitioned labeled datasets.
  org.apache.ignite.ml.trainers | Contains model trainers.
  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.util.genetic | Contains some genetic algorithms for discrete optimization task in ML module locally.

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

    Fields in org.apache.ignite.ml.composition.boosting declared as LearningEnvironment
    Modifier and Type | Field and Description
    --- | ---
    `protected LearningEnvironment` | GDBLearningStrategy.`trainerEnvironment` Learning environment used for trainer.

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

    Methods in org.apache.ignite.ml.dataset with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `UpstreamTransformer` | UpstreamTransformerBuilder.`build(LearningEnvironment env)` Create `UpstreamTransformer` based on learning environment.
    `<C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetBuilder.`build(LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment localLearningEnv)` Constructs a new instance of `Dataset` that includes allocation required data structures and initialization of `context` part of partitions.
    `C` | PartitionContextBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize)` Builds a new partition `context` from an `upstream` data.
    `D` | PartitionDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.
    `default C` | PartitionContextBuilder.`build(LearningEnvironment env, Stream<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize)` Builds a new partition `context` from an `upstream` data.
    `default D` | PartitionDataBuilder.`build(LearningEnvironment env, Stream<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(Ignite ignite, IgniteCache<K,V> upstreamCache, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>` | DatasetFactory.`create(Map<K,V> upstreamMap, LearningEnvironmentBuilder envBuilder, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of local dataset using the specified `partCtxBuilder` and `partDataBuilder`.

    Method parameters in org.apache.ignite.ml.dataset with type arguments of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `default void` | Dataset.`compute(IgniteBiConsumer<D,LearningEnvironment> map)` Applies the specified `map` function to every partition `data` in the dataset and `LearningEnvironment`.
    `default <R> R` | Dataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | Dataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `default void` | Dataset.`computeWithCtx(IgniteTriConsumer<C,D,LearningEnvironment> map)` Applies the specified `map` function to every partition `data`, `context` and `LearningEnvironment` in the dataset.
    `default <R> R` | Dataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce)` Applies the specified `map` function to every partition `data`, `context` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | Dataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

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

    Methods in org.apache.ignite.ml.dataset.impl.bootstrapping with parameters of type LearningEnvironment
    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 LearningEnvironment in org.apache.ignite.ml.dataset.impl.cache

    Methods in org.apache.ignite.ml.dataset.impl.cache with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<C extends Serializable,D extends AutoCloseable> CacheBasedDataset<K,V,C,D>` | CacheBasedDatasetBuilder.`build(LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment localLearningEnv)` Constructs a new instance of `Dataset` that includes allocation required data structures and initialization of `context` part of partitions.

    Method parameters in org.apache.ignite.ml.dataset.impl.cache with type arguments of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | CacheBasedDataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | CacheBasedDataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

    Constructors in org.apache.ignite.ml.dataset.impl.cache with parameters of type LearningEnvironment
    Constructor and Description
    `CacheBasedDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, UpstreamTransformerBuilder upstreamTransformerBuilder, IgniteCache<Integer,C> datasetCache, LearningEnvironmentBuilder envBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, UUID datasetId, boolean upstreamKeepBinary, LearningEnvironment localLearningEnv, int retriesCnt)` Constructs a new instance of dataset based on Ignite Cache, which is used as `upstream` and as reliable storage for partition `context` as well.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.dataset.impl.cache.util

    Methods in org.apache.ignite.ml.dataset.impl.cache.util that return LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `static LearningEnvironment` | ComputeUtils.`getLearningEnvironment(Ignite ignite, UUID datasetId, int part, LearningEnvironmentBuilder envBuilder)` Gets learning environment for given partition.

    Methods in org.apache.ignite.ml.dataset.impl.cache.util with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V,C extends Serializable,D extends AutoCloseable> D` | ComputeUtils.`getData(Ignite ignite, String upstreamCacheName, IgniteBiPredicate<K,V> filter, UpstreamTransformerBuilder transformerBuilder, String datasetCacheName, UUID datasetId, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment env, boolean isKeepBinary)` Extracts partition `data` from the local storage, if it's not found in local storage recovers this `data` from a partition `upstream` and `context`.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.dataset.impl.local

    Methods in org.apache.ignite.ml.dataset.impl.local with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<C extends Serializable,D extends AutoCloseable> LocalDataset<C,D>` | LocalDatasetBuilder.`build(LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment learningEnvironment)` Constructs a new instance of `Dataset` that includes allocation required data structures and initialization of `context` part of partitions.

    Method parameters in org.apache.ignite.ml.dataset.impl.local with type arguments of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | LocalDataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | LocalDataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.dataset.primitive

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

    Method parameters in org.apache.ignite.ml.dataset.primitive with type arguments of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | DatasetWrapper.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | DatasetWrapper.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

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

    Methods in org.apache.ignite.ml.dataset.primitive.builder.context with parameters of type LearningEnvironment
    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 LearningEnvironment in org.apache.ignite.ml.dataset.primitive.builder.data

    Methods in org.apache.ignite.ml.dataset.primitive.builder.data with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `SimpleLabeledDatasetData` | SimpleLabeledDatasetDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.
    `SimpleDatasetData` | SimpleDatasetDataBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.environment

    Fields in org.apache.ignite.ml.environment declared as LearningEnvironment
    Modifier and Type | Field and Description
    --- | ---
    `static LearningEnvironment` | LearningEnvironment.`DEFAULT_TRAINER_ENV` Default environment

    Methods in org.apache.ignite.ml.environment that return LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `default LearningEnvironment` | LearningEnvironmentBuilder.`buildForTrainer()` Builds learning environment for trainer.
    `LearningEnvironment` | LearningEnvironmentBuilder.`buildForWorker(int part)` Builds `LearningEnvironment` for worker on given partition.
    `LearningEnvironment` | DefaultLearningEnvironmentBuilder.`buildForWorker(int part)` Builds `LearningEnvironment` for worker on given partition.

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

    Methods in org.apache.ignite.ml.knn with parameters of type LearningEnvironment
    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`.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.math.isolve.lsqr

    Constructors in org.apache.ignite.ml.math.isolve.lsqr with parameters of type LearningEnvironment
    Constructor and Description
    `LSQROnHeap(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionDataBuilder<K,V,LSQRPartitionContext,SimpleLabeledDatasetData> partDataBuilder, LearningEnvironment localLearningEnv)` Constructs a new instance of OnHeap LSQR algorithm implementation.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.preprocessing

    Methods in org.apache.ignite.ml.preprocessing that return LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `default LearningEnvironment` | PreprocessingTrainer.`learningEnvironment(Preprocessor<K,V> basePreprocessor)` Returns local learning environment with initialized deploying context by base preprocessor.

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

    Methods in org.apache.ignite.ml.recommendation with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `RecommendationTrainer` | RecommendationTrainer.`withTrainerEnvironment(LearningEnvironment trainerEnvironment)` Set up trainer learning environment.

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

    Methods in org.apache.ignite.ml.recommendation.util with parameters of type LearningEnvironment
    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 LearningEnvironment in org.apache.ignite.ml.selection.cv

    Fields in org.apache.ignite.ml.selection.cv declared as LearningEnvironment
    Modifier and Type | Field and Description
    --- | ---
    `protected LearningEnvironment` | AbstractCrossValidation.`environment` Learning Environment.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.sparkmodelparser

    Methods in org.apache.ignite.ml.sparkmodelparser with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `static Model` | SparkModelParser.`parse(String pathToMdl, SupportedSparkModels parsedSparkMdl, LearningEnvironment learningEnvironment)` Load model from parquet (presented as a directory).

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

    Methods in org.apache.ignite.ml.structures.partition with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `LabelPartitionDataOnHeap` | LabelPartitionDataBuilderOnHeap.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.
    `LabeledVectorSet<LabeledVector>` | LabeledDatasetPartitionDataBuilderOnHeap.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

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

    Fields in org.apache.ignite.ml.trainers declared as LearningEnvironment
    Modifier and Type | Field and Description
    --- | ---
    `protected LearningEnvironment` | DatasetTrainer.`environment` Learning Environment.

    Methods in org.apache.ignite.ml.trainers that return LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `LearningEnvironment` | DatasetTrainer.`learningEnvironment()` Get learning environment.

    Methods in org.apache.ignite.ml.trainers with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> M` | DatasetTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, LearningEnvironment learningEnvironment)` Trains model based on the specified data.

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

    Methods in org.apache.ignite.ml.tree.data with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `DecisionTreeData` | DecisionTreeDataBuilder.`build(LearningEnvironment envBuilder, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, C ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

  - ### Uses of LearningEnvironment in org.apache.ignite.ml.util.genetic

    Methods in org.apache.ignite.ml.util.genetic with parameters of type LearningEnvironment
    Modifier and Type | Method and Description
    --- | ---
    `void` | GeneticAlgorithm.`runParallel(LearningEnvironment environment)` The main method for genetic algorithm.

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Release Date | : April 25 2023