Uses of Interface
org.apache.ignite.ml.dataset.Dataset

Packages that use Dataset

Package	Description
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.cache	Base package for cache based implementation of machine learning 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.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.knn.utils	Contains util functionality for kNN algorithms.
org.apache.ignite.ml.nn	Contains neural networks and related classes.
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 Dataset in org.apache.ignite.ml.composition.boosting.convergence

Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.composition.boosting.convergence.mean

Methods in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.composition.boosting.convergence.median

Methods in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.composition.boosting.convergence.simple

Methods in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.dataset

Methods in org.apache.ignite.ml.dataset with type parameters of type Dataset

Modifier and Type	Method and Description
default <I extends Dataset<C,D>> I	Dataset.wrap(IgniteFunction<Dataset<C,D>,I> wrapper) Wraps this dataset into the specified wrapper to introduce new functionality based on compute and computeWithCtx methods.

Methods in org.apache.ignite.ml.dataset that return Dataset

Modifier and Type	Method and Description
<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.
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(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder) 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(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder) 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 Dataset

Modifier and Type	Method and Description
default <I extends Dataset<C,D>> I	Dataset.wrap(IgniteFunction<Dataset<C,D>,I> wrapper) Wraps this dataset into the specified wrapper to introduce new functionality based on compute and computeWithCtx methods.

Uses of Dataset in org.apache.ignite.ml.dataset.impl.cache

Classes in org.apache.ignite.ml.dataset.impl.cache that implement Dataset

Modifier and Type	Class and Description
class	CacheBasedDataset<K,V,C extends Serializable,D extends AutoCloseable> An implementation of dataset based on Ignite Cache, which is used as upstream and as reliable storage for partition context as well.

Uses of Dataset in org.apache.ignite.ml.dataset.impl.local

Classes in org.apache.ignite.ml.dataset.impl.local that implement Dataset

Modifier and Type	Class and Description
class	LocalDataset<C extends Serializable,D extends AutoCloseable> An implementation of dataset based on local data structures such as Map and List and doesn't require Ignite environment.

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

Classes in org.apache.ignite.ml.dataset.primitive that implement Dataset

Modifier and Type	Class and Description
class	DatasetWrapper<C extends Serializable,D extends AutoCloseable> A dataset wrapper that allows to introduce new functionality based on common compute methods.
class	SimpleDataset<C extends Serializable> A simple dataset introduces additional methods based on a matrix of features.
class	SimpleLabeledDataset<C extends Serializable> A simple labeled dataset introduces additional methods based on a matrix of features and labels vector.

Fields in org.apache.ignite.ml.dataset.primitive declared as Dataset

Modifier and Type	Field and Description
protected Dataset<C,D>	DatasetWrapper.delegate Delegate that performs compute actions.

Constructors in org.apache.ignite.ml.dataset.primitive with parameters of type Dataset

Constructor and Description
DatasetWrapper(Dataset<C,D> delegate) Constructs a new instance of dataset wrapper that delegates compute actions to the actual delegate.
SimpleDataset(Dataset<C,SimpleDatasetData> delegate) Creates a new instance of simple dataset that introduces additional methods based on a matrix of features.
SimpleLabeledDataset(Dataset<C,SimpleLabeledDatasetData> delegate) Creates a new instance of simple labeled dataset that introduces additional methods based on a matrix of features and labels vector.

Uses of Dataset in org.apache.ignite.ml.knn

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

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).

Constructors in org.apache.ignite.ml.knn with parameters of type Dataset

Constructor and Description
KNNModel(Dataset<EmptyContext,SpatialIndex<L>> dataset, DistanceMeasure distanceMeasure, int k, boolean weighted) Constructs a new instance of KNN model.

Uses of Dataset in org.apache.ignite.ml.knn.classification

Methods in org.apache.ignite.ml.knn.classification with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.knn.regression

Methods in org.apache.ignite.ml.knn.regression with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.knn.utils

Methods in org.apache.ignite.ml.knn.utils that return Dataset

Modifier and Type	Method and Description
static <K,V,C extends Serializable> Dataset<EmptyContext,LabeledVectorSet<LabeledVector>>	KNNUtils.buildDataset(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer) Builds dataset.

Uses of Dataset in org.apache.ignite.ml.nn

Methods in org.apache.ignite.ml.nn that return types with arguments of type Dataset

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 Dataset

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 Dataset

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 Dataset in org.apache.ignite.ml.tree

Methods in org.apache.ignite.ml.tree with parameters of type Dataset

Modifier and Type	Method and Description
<K,V> DecisionTreeNode	DecisionTree.fit(Dataset<EmptyContext,DecisionTreeData> dataset)
protected ImpurityMeasureCalculator<GiniImpurityMeasure>	DecisionTreeClassificationTrainer.getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset) Returns impurity measure calculator.
protected abstract ImpurityMeasureCalculator<T>	DecisionTree.getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset) Returns impurity measure calculator.
protected ImpurityMeasureCalculator<MSEImpurityMeasure>	DecisionTreeRegressionTrainer.getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset) Returns impurity measure calculator.

Uses of Dataset in org.apache.ignite.ml.tree.leaf

Methods in org.apache.ignite.ml.tree.leaf with parameters of type Dataset

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

Methods in org.apache.ignite.ml.tree.randomforest with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.tree.randomforest.data.impurity

Methods in org.apache.ignite.ml.tree.randomforest.data.impurity with parameters of type Dataset

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 Dataset in org.apache.ignite.ml.tree.randomforest.data.statistics

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

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.