Uses of Class
org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

Packages that use BootstrappedDatasetPartition

Package	Description
org.apache.ignite.ml.dataset.impl.bootstrapping	Base package for bootstrapped implementation of machine learning dataset.
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 BootstrappedDatasetPartition in org.apache.ignite.ml.dataset.impl.bootstrapping

Methods in org.apache.ignite.ml.dataset.impl.bootstrapping that return BootstrappedDatasetPartition

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 BootstrappedDatasetPartition in org.apache.ignite.ml.tree.randomforest

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

Modifier and Type	Method and Description
protected boolean	RandomForestClassifierTrainer.init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset) Aggregates all unique labels from dataset and assigns integer id value for each label.
protected boolean	RandomForestTrainer.init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset) Init-step before learning.

Uses of BootstrappedDatasetPartition 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 BootstrappedDatasetPartition

Modifier and Type	Method and Description
Map<NodeId,ImpurityHistogramsComputer.NodeImpurityHistograms<S>>	ImpurityHistogramsComputer.aggregateImpurityStatistics(ArrayList<TreeRoot> roots, Map<Integer,BucketMeta> histMeta, Map<NodeId,TreeNode> nodesToLearn, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset) Computes histograms for each feature.

Uses of BootstrappedDatasetPartition in org.apache.ignite.ml.tree.randomforest.data.statistics

Methods in org.apache.ignite.ml.tree.randomforest.data.statistics with parameters of type BootstrappedDatasetPartition

Modifier and Type	Method and Description
List<NormalDistributionStatistics>	NormalDistributionStatisticsComputer.computeStatsOnPartition(BootstrappedDatasetPartition part, List<FeatureMeta> meta) Aggregates normal distribution statistics for continual features in dataset partition.

Method parameters in org.apache.ignite.ml.tree.randomforest.data.statistics with type arguments of type BootstrappedDatasetPartition

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<TreeRoot> 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.