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 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.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.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.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> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of local SimpleDataset using the specified featureExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) 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(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(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.classification

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

Constructor and Description
KNNClassificationModel(Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> dataset) Builds the model via prepared dataset.

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

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

Constructor and Description
KNNRegressionModel(Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> dataset) Builds the model via prepared dataset.

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.tree

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

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 ImpurityMeasureCalculator<MSEImpurityMeasure>	DecisionTreeRegressionTrainer.getImpurityMeasureCalculator(Dataset<EmptyContext,DecisionTreeData> dataset) Returns impurity measure calculator.
protected abstract ImpurityMeasureCalculator<T>	DecisionTree.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	MostCommonDecisionTreeLeafBuilder.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	DecisionTreeLeafBuilder.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	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 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<TreeRoot> roots, Map<Integer,BucketMeta> histMeta, Map<NodeId,TreeNode> nodesToLearn, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset) Computes histograms for each features.

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<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.