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.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
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.
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.

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	DefaultLearningEnvironmentBuilder.buildForWorker(int part) Builds LearningEnvironment for worker on given partition.
LearningEnvironment	LearningEnvironmentBuilder.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 <K,V> LearningEnvironment	PreprocessingTrainer.learningEnvironment(Preprocessor<K,V> basePreprocessor) Returns local learning environment with initialized deploying context by base preptocessor.

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.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.