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

Packages that use DatasetBuilder

Package	Description
org.apache.ignite.ml.clustering.gmm	Contains Gauss Mixture Model clustering algorithm (see GmmModel).
org.apache.ignite.ml.clustering.kmeans	Contains kMeans clustering algorithm.
org.apache.ignite.ml.composition.bagging	Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and return a bagged version of them.
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.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.composition.combinators.parallel	Contains parallel combinators of trainers and models.
org.apache.ignite.ml.composition.combinators.sequential	Contains sequential combinators of trainers and models.
org.apache.ignite.ml.composition.stacking	Contains classes used for training with stacking technique.
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.knn	Contains main APIs for kNN algorithms.
org.apache.ignite.ml.knn.ann	Contains main APIs for ANN classification algorithms.
org.apache.ignite.ml.knn.utils	Contains util functionality for kNN algorithms.
org.apache.ignite.ml.math.isolve.lsqr	Contains LSQR algorithm implementation.
org.apache.ignite.ml.multiclass	Contains various multi-classifier models and trainers.
org.apache.ignite.ml.naivebayes.discrete	Contains Bernoulli naive Bayes classifier.
org.apache.ignite.ml.naivebayes.gaussian	Contains Gaussian naive Bayes classifier.
org.apache.ignite.ml.nn	Contains neural networks and related classes.
org.apache.ignite.ml.pipeline	Contains Pipeline API.
org.apache.ignite.ml.preprocessing	Base package for machine learning preprocessing classes.
org.apache.ignite.ml.preprocessing.binarization	Contains binarization preprocessor.
org.apache.ignite.ml.preprocessing.encoding	Contains encoding preprocessors.
org.apache.ignite.ml.preprocessing.imputing	Contains Imputer preprocessor.
org.apache.ignite.ml.preprocessing.maxabsscaling	Contains Max Abs Scaler preprocessor.
org.apache.ignite.ml.preprocessing.minmaxscaling	Contains Min Max Scaler preprocessor.
org.apache.ignite.ml.preprocessing.normalization	Contains Normalizer preprocessor.
org.apache.ignite.ml.preprocessing.standardscaling	Contains Standard scaler preprocessor.
org.apache.ignite.ml.recommendation	Contains recommendation system framework.
org.apache.ignite.ml.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.regressions.logistic	Contains various logistic regressions.
org.apache.ignite.ml.selection.cv	Root package for cross-validation algorithms.
org.apache.ignite.ml.sql	Contains util classes that help to work with machine learning models in SQL and train models on data from SQL tables.
org.apache.ignite.ml.svm	Contains main APIs for SVM(support vector machines) algorithms.
org.apache.ignite.ml.trainers	Contains model trainers.
org.apache.ignite.ml.tree	Root package for decision trees.
org.apache.ignite.ml.tree.boosting	Contains implementation of gradient boosting on trees.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.
org.apache.ignite.ml.util.generators	Contains utility classes for data streams generation.

Uses of DatasetBuilder in org.apache.ignite.ml.clustering.gmm

Methods in org.apache.ignite.ml.clustering.gmm with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> GmmModel	GmmTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> GmmModel	GmmTrainer.updateModel(GmmModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.clustering.kmeans

Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> KMeansModel	KMeansTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <K,V> KMeansModel	KMeansTrainer.updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.bagging

Methods in org.apache.ignite.ml.composition.bagging with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> BaggedModel	BaggedTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> BaggedModel	BaggedTrainer.update(BaggedModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> BaggedModel	BaggedTrainer.updateModel(BaggedModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.boosting

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

Modifier and Type	Method and Description
protected <V,K,C extends Serializable> IgniteBiTuple<Double,Long>	GDBTrainer.computeInitialValue(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor) Compute mean value of label as first approximation.
<K,V> ModelsComposition	GDBTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <V,K> boolean	GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor) Defines unique labels in dataset if need (useful in case of classification).
protected abstract <V,K> boolean	GDBTrainer.learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor) Defines unique labels in dataset if need (useful in case of classification).
<K,V> List<IgniteModel<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Implementation of gradient boosting iterations.
<K,V> List<IgniteModel<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> ModelsComposition	GDBTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.boosting.convergence

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

Modifier and Type	Method and Description
abstract <K,V> ConvergenceChecker<K,V>	ConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer) Create an instance of ConvergenceChecker.
boolean	ConvergenceChecker.isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Constructors in org.apache.ignite.ml.composition.boosting.convergence with parameters of type DatasetBuilder

Constructor and Description
ConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision) Constructs an instance of ConvergenceChecker.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.boosting.convergence.mean

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MeanAbsValueConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Create an instance of ConvergenceChecker.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type DatasetBuilder

Constructor and Description
MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision) Creates an instance of MeanAbsValueConvergenceChecker.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.boosting.convergence.median

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MedianOfMedianConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Create an instance of ConvergenceChecker.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type DatasetBuilder

Constructor and Description
MedianOfMedianConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> lblMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision) Creates an instance of MedianOfMedianConvergenceChecker.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.boosting.convergence.simple

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	ConvergenceCheckerStubFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Create an instance of ConvergenceChecker.
boolean	ConvergenceCheckerStub.isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type DatasetBuilder

Constructor and Description
ConvergenceCheckerStub(long sampleSize, IgniteFunction externalLbToInternalMapping, Loss loss, DatasetBuilder datasetBuilder, Preprocessor<K,V> preprocessor, double precision) Creates an instance of ConvergenceCheckerStub.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.combinators.parallel

Methods in org.apache.ignite.ml.composition.combinators.parallel with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.update(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.updateModel(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.combinators.sequential

Methods in org.apache.ignite.ml.composition.combinators.sequential with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.update(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.updateModel(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Uses of DatasetBuilder in org.apache.ignite.ml.composition.stacking

Methods in org.apache.ignite.ml.composition.stacking with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.update(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.updateModel(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Uses of DatasetBuilder in org.apache.ignite.ml.dataset

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

Modifier and Type	Method and Description
DatasetBuilder<K,V>	DatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.
DatasetBuilder<K,V>	DatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder) Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

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

Modifier and Type	Method and Description
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,CO extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> vectorizer) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> vectorizer) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.

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

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

Modifier and Type	Class and Description
class	CacheBasedDatasetBuilder<K,V> A dataset builder that makes CacheBasedDataset.

Methods in org.apache.ignite.ml.dataset.impl.cache that return DatasetBuilder

Modifier and Type	Method and Description
DatasetBuilder<K,V>	CacheBasedDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.
DatasetBuilder<K,V>	CacheBasedDatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder) Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

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

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

Modifier and Type	Class and Description
class	LocalDatasetBuilder<K,V> A dataset builder that makes LocalDataset.

Methods in org.apache.ignite.ml.dataset.impl.local that return DatasetBuilder

Modifier and Type	Method and Description
DatasetBuilder<K,V>	LocalDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.
DatasetBuilder<K,V>	LocalDatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder) Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

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

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

Modifier and Type	Method and Description
protected <K,V> M	KNNTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <K,V> M	KNNTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.knn.ann

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

Modifier and Type	Method and Description
<K,V> ANNClassificationModel	ANNClassificationTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> ANNClassificationModel	ANNClassificationTrainer.updateModel(ANNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.knn.utils

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

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 DatasetBuilder in org.apache.ignite.ml.math.isolve.lsqr

Constructors in org.apache.ignite.ml.math.isolve.lsqr with parameters of type DatasetBuilder

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 DatasetBuilder in org.apache.ignite.ml.multiclass

Methods in org.apache.ignite.ml.multiclass with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> MultiClassModel<M>	OneVsRestTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> MultiClassModel<M>	OneVsRestTrainer.updateModel(MultiClassModel<M> newMdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.naivebayes.discrete

Methods in org.apache.ignite.ml.naivebayes.discrete with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> DiscreteNaiveBayesModel	DiscreteNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> DiscreteNaiveBayesModel	DiscreteNaiveBayesTrainer.updateModel(DiscreteNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.naivebayes.gaussian

Methods in org.apache.ignite.ml.naivebayes.gaussian with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> GaussianNaiveBayesModel	GaussianNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> GaussianNaiveBayesModel	GaussianNaiveBayesTrainer.updateModel(GaussianNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

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

Methods in org.apache.ignite.ml.nn with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> MultilayerPerceptron	MLPTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> MultilayerPerceptron	MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.pipeline

Methods in org.apache.ignite.ml.pipeline with parameters of type DatasetBuilder

Modifier and Type	Method and Description
PipelineMdl<K,V>	Pipeline.fit(DatasetBuilder datasetBuilder) Fits the pipeline to the input dataset builder.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing

Methods in org.apache.ignite.ml.preprocessing with parameters of type DatasetBuilder

Modifier and Type	Method and Description
default Preprocessor<K,V>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.binarization

Methods in org.apache.ignite.ml.preprocessing.binarization with parameters of type DatasetBuilder

Modifier and Type	Method and Description
BinarizationPreprocessor<K,V>	BinarizationTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.encoding

Methods in org.apache.ignite.ml.preprocessing.encoding with parameters of type DatasetBuilder

Modifier and Type	Method and Description
EncoderPreprocessor<K,V>	EncoderTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.imputing

Methods in org.apache.ignite.ml.preprocessing.imputing with parameters of type DatasetBuilder

Modifier and Type	Method and Description
ImputerPreprocessor<K,V>	ImputerTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.maxabsscaling

Methods in org.apache.ignite.ml.preprocessing.maxabsscaling with parameters of type DatasetBuilder

Modifier and Type	Method and Description
MaxAbsScalerPreprocessor<K,V>	MaxAbsScalerTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.minmaxscaling

Methods in org.apache.ignite.ml.preprocessing.minmaxscaling with parameters of type DatasetBuilder

Modifier and Type	Method and Description
MinMaxScalerPreprocessor<K,V>	MinMaxScalerTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.normalization

Methods in org.apache.ignite.ml.preprocessing.normalization with parameters of type DatasetBuilder

Modifier and Type	Method and Description
NormalizationPreprocessor<K,V>	NormalizationTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.standardscaling

Methods in org.apache.ignite.ml.preprocessing.standardscaling with parameters of type DatasetBuilder

Modifier and Type	Method and Description
StandardScalerPreprocessor<K,V>	StandardScalerTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.recommendation

Methods in org.apache.ignite.ml.recommendation with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,O extends Serializable,S extends Serializable> RecommendationModel<O,S>	RecommendationTrainer.fit(DatasetBuilder<K,? extends ObjectSubjectRatingTriplet<O,S>> datasetBuilder) Fits prediction model.
RecommendationModel<Serializable,Serializable>	RecommendationTrainer.fit(DatasetBuilder<Object,BinaryObject> datasetBuilder, String objFieldName, String subjFieldName, String ratingFieldName) Fits prediction model on a data storen in binary format.

Uses of DatasetBuilder in org.apache.ignite.ml.regressions.linear

Methods in org.apache.ignite.ml.regressions.linear with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
protected <K,V> LinearRegressionModel	LinearRegressionSGDTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.regressions.logistic

Methods in org.apache.ignite.ml.regressions.logistic with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.selection.cv

Method parameters in org.apache.ignite.ml.selection.cv with type arguments of type DatasetBuilder

Modifier and Type	Method and Description
protected double[]	AbstractCrossValidation.score(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier, BiFunction<IgniteBiPredicate<K,V>,M,LabelPairCursor<L>> testDataIterSupplier) Computes cross-validated metrics.
protected double[]	AbstractCrossValidation.scorePipeline(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier, BiFunction<IgniteBiPredicate<K,V>,M,LabelPairCursor<L>> testDataIterSupplier) Computes cross-validated metrics.

Uses of DatasetBuilder in org.apache.ignite.ml.sql

Classes in org.apache.ignite.ml.sql that implement DatasetBuilder

Modifier and Type	Class and Description
class	SqlDatasetBuilder Subclass of CacheBasedDatasetBuilder that should be used to work with SQL tables.

Uses of DatasetBuilder in org.apache.ignite.ml.svm

Methods in org.apache.ignite.ml.svm with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> SVMLinearClassificationModel	SVMLinearClassificationTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <K,V> SVMLinearClassificationModel	SVMLinearClassificationTrainer.updateModel(SVMLinearClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.trainers

Methods in org.apache.ignite.ml.trainers with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, LearningEnvironment learningEnvironment) Trains model based on the specified data.
<K,V> AdaptableDatasetModel<I,O,IW,OW,M>	AdaptableDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected abstract <K,V> M	DatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.update(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> AdaptableDatasetModel<I,O,IW,OW,M>	AdaptableDatasetTrainer.updateModel(AdaptableDatasetModel<I,O,IW,OW,M> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
protected abstract <K,V> M	DatasetTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.tree

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

Modifier and Type	Method and Description
<K,V> DecisionTreeNode	DecisionTree.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <K,V> DecisionTreeNode	DecisionTree.updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model based on dataset because there is no valid approach to update decision trees.

Uses of DatasetBuilder in org.apache.ignite.ml.tree.boosting

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

Modifier and Type	Method and Description
<K,V> List<IgniteModel<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

Uses of DatasetBuilder in org.apache.ignite.ml.tree.randomforest

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

Modifier and Type	Method and Description
<K,V> ModelsComposition	RandomForestTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains new model taken previous one as a first approximation.

Uses of DatasetBuilder in org.apache.ignite.ml.util.generators

Methods in org.apache.ignite.ml.util.generators that return DatasetBuilder

Modifier and Type	Method and Description
default DatasetBuilder<Vector,Double>	DataStreamGenerator.asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions) Convert first N values from stream to DatasetBuilder.
default DatasetBuilder<Vector,Double>	DataStreamGenerator.asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions, UpstreamTransformerBuilder upstreamTransformerBuilder) Convert first N values from stream to DatasetBuilder.
default DatasetBuilder<Vector,Double>	DataStreamGenerator.asDatasetBuilder(int datasetSize, int partitions) Convert first N values from stream to DatasetBuilder.