Uses of Interface
org.apache.ignite.ml.preprocessing.Preprocessor

Packages that use Preprocessor

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.feature.extractor	Package for upstream object vectorizations.
org.apache.ignite.ml.dataset.feature.extractor.impl	Package contains default implementations of Vectorizer.
org.apache.ignite.ml.dataset.impl.bootstrapping	Base package for bootstrapped 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.data	Contains partition data builders.
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.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.developer	Contains Developer API preprocessors.
org.apache.ignite.ml.preprocessing.encoding	Contains encoding preprocessors.
org.apache.ignite.ml.preprocessing.encoding.onehotencoder	Contains one hot encoding preprocessor.
org.apache.ignite.ml.preprocessing.encoding.stringencoder	Contains string encoding preprocessor.
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.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.selection.scoring.cursor	Util classes used for score calculation.
org.apache.ignite.ml.selection.scoring.evaluator	Package for model evaluator classes.
org.apache.ignite.ml.structures.partition	Contains internal APIs for dataset partitioned labeled datasets.
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.data	Contains data and data builder required for decision tree trainers built on top of partition based dataset.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.

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

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

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 Preprocessor in org.apache.ignite.ml.clustering.kmeans

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

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 Preprocessor in org.apache.ignite.ml.composition.bagging

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

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 Preprocessor in org.apache.ignite.ml.composition.boosting

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

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 Preprocessor in org.apache.ignite.ml.composition.boosting.convergence

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

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.

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

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

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

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 Preprocessor

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

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

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 Preprocessor

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

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

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.

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

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 Preprocessor in org.apache.ignite.ml.composition.combinators.parallel

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

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 Preprocessor in org.apache.ignite.ml.composition.combinators.sequential

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

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 Preprocessor in org.apache.ignite.ml.composition.stacking

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

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 Preprocessor in org.apache.ignite.ml.dataset

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

Modifier and Type	Method and Description
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> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, 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(Ignite ignite, IgniteCache<K,V> upstreamCache, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable,CO extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> featureExtractor) Creates a new instance of local SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor) Creates a new instance of local 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.
static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, 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(Ignite ignite, LearningEnvironmentBuilder envBuilder, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> vectorizer) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> vectorizer) Creates a new instance of local SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, LearningEnvironmentBuilder envBuilder, int partitions, Preprocessor<K,V> vectorizer) Creates a new instance of local SimpleLabeledDataset using the specified featureExtractor and lbExtractor.

Uses of Preprocessor in org.apache.ignite.ml.dataset.feature.extractor

Classes in org.apache.ignite.ml.dataset.feature.extractor that implement Preprocessor

Modifier and Type	Class and Description
static class	ExtractionUtils.ArrayLikeVectorizer<K,V> Vectorizer extracting vectors from array-like structure with finite size and integer coordinates.
static class	ExtractionUtils.DefaultLabelVectorizer<K,V,C extends Serializable> Vectorizer with double-label containing on same level as feature values.
static class	ExtractionUtils.IntCoordVectorizer<K,V> Vectorizer with integer coordinates.
static class	ExtractionUtils.StringCoordVectorizer<K,V> Vectorizer with String-coordinates.
class	Vectorizer<K,V,C extends Serializable,L> Class for extracting labeled vectors from upstream.
static class	Vectorizer.VectorizerAdapter<K,V,C extends Serializable,L> Utility class for convenient overridings.

Uses of Preprocessor in org.apache.ignite.ml.dataset.feature.extractor.impl

Classes in org.apache.ignite.ml.dataset.feature.extractor.impl that implement Preprocessor

Modifier and Type	Class and Description
class	BinaryObjectVectorizer<K> Vectorizer on binary objects.
class	DoubleArrayVectorizer<K> Vectorizer on arrays of doubles.
class	DummyVectorizer<K> Vectorizer on Vector.
class	LabeledDummyVectorizer<K,L> Vectorizer on LabeledVector.

Uses of Preprocessor in org.apache.ignite.ml.dataset.impl.bootstrapping

Constructors in org.apache.ignite.ml.dataset.impl.bootstrapping with parameters of type Preprocessor

Constructor and Description
BootstrappedDatasetBuilder(Preprocessor<K,V> preprocessor, int samplesCnt, double subsampleSize) Creates an instance of BootstrappedDatasetBuilder.

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

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

Constructor and Description
FeatureMatrixWithLabelsOnHeapDataBuilder(Preprocessor<K,V> preprocessor) Constructs a new instance of decision tree data builder.

Uses of Preprocessor in org.apache.ignite.ml.dataset.primitive.builder.data

Constructors in org.apache.ignite.ml.dataset.primitive.builder.data with parameters of type Preprocessor

Constructor and Description
SimpleDatasetDataBuilder(Preprocessor<K,V> preprocessor) Construct a new instance of partition data builder that makes SimpleDatasetData.
SimpleLabeledDatasetDataBuilder(Preprocessor<K,V> vectorizer) Constructs a new instance of partition data builder that makes SimpleLabeledDatasetData.

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

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

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.

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

Constructor and Description
KNNPartitionDataBuilder(Preprocessor<K,V> preprocessor, SpatialIndexType spatialIdxType, DistanceMeasure distanceMeasure) Constructs a new instance of KNN partition data builder.

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

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

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

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

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 Preprocessor in org.apache.ignite.ml.multiclass

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

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 Preprocessor in org.apache.ignite.ml.naivebayes.discrete

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

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 Preprocessor in org.apache.ignite.ml.naivebayes.gaussian

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

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 Preprocessor in org.apache.ignite.ml.nn

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

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 Preprocessor in org.apache.ignite.ml.pipeline

Methods in org.apache.ignite.ml.pipeline that return Preprocessor

Modifier and Type	Method and Description
Preprocessor<K,V>	PipelineMdl.getPreprocessor()

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

Modifier and Type	Method and Description
PipelineMdl<K,V>	PipelineMdl.withPreprocessor(Preprocessor<K,V> preprocessor)

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

Methods in org.apache.ignite.ml.preprocessing that return Preprocessor

Modifier and Type	Method and Description
default Preprocessor<K,V>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default <L1,L2> Preprocessor<K,V>	Preprocessor.map(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> func) Map vectorizer answer.

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

Modifier and Type	Method and Description
default Preprocessor<K,V>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder, Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default Preprocessor<K,V>	PreprocessingTrainer.fit(Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor) Fits preprocessor.
default <K,V> LearningEnvironment	PreprocessingTrainer.learningEnvironment(Preprocessor<K,V> basePreprocessor) Returns local learning environment with initialized deploying context by base preptocessor.

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

Classes in org.apache.ignite.ml.preprocessing.binarization that implement Preprocessor

Modifier and Type	Class and Description
class	BinarizationPreprocessor<K,V> Preprocessing function that makes binarization.

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

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

Constructors in org.apache.ignite.ml.preprocessing.binarization with parameters of type Preprocessor

Constructor and Description
BinarizationPreprocessor(double threshold, Preprocessor<K,V> basePreprocessor) Constructs a new instance of Binarization preprocessor.

Uses of Preprocessor in org.apache.ignite.ml.preprocessing.developer

Classes in org.apache.ignite.ml.preprocessing.developer that implement Preprocessor

Modifier and Type	Class and Description
class	MappedPreprocessor<K,V,L0,L1> Mapped Preprocessor.
class	PatchedPreprocessor<K,V,L1,L2> Preprocessing function that makes binarization.

Fields in org.apache.ignite.ml.preprocessing.developer declared as Preprocessor

Modifier and Type	Field and Description
protected Preprocessor<K,V>	MappedPreprocessor.original Original preprocessor.

Constructors in org.apache.ignite.ml.preprocessing.developer with parameters of type Preprocessor

Constructor and Description
MappedPreprocessor(Preprocessor<K,V> original, IgniteFunction<LabeledVector<L0>,LabeledVector<L1>> andThen) Creates an instance of MappedPreprocessor.
PatchedPreprocessor(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> lbPatcher, Preprocessor<K,V> basePreprocessor) Constructs a new instance of Binarization preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.encoding that implement Preprocessor

Modifier and Type	Class and Description
class	EncoderPreprocessor<K,V> Preprocessing function that makes encoding.

Fields in org.apache.ignite.ml.preprocessing.encoding declared as Preprocessor

Modifier and Type	Field and Description
protected Preprocessor<K,V>	EncoderPreprocessor.basePreprocessor Base preprocessor.

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

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

Constructors in org.apache.ignite.ml.preprocessing.encoding with parameters of type Preprocessor

Constructor and Description
EncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices) Constructs a new instance of String Encoder preprocessor.

Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.onehotencoder

Classes in org.apache.ignite.ml.preprocessing.encoding.onehotencoder that implement Preprocessor

Modifier and Type	Class and Description
class	OneHotEncoderPreprocessor<K,V> Preprocessing function that makes one-hot encoding.

Constructors in org.apache.ignite.ml.preprocessing.encoding.onehotencoder with parameters of type Preprocessor

Constructor and Description
OneHotEncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices) Constructs a new instance of One Hot Encoder preprocessor.

Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.stringencoder

Classes in org.apache.ignite.ml.preprocessing.encoding.stringencoder that implement Preprocessor

Modifier and Type	Class and Description
class	StringEncoderPreprocessor<K,V> Preprocessing function that makes String encoding.

Constructors in org.apache.ignite.ml.preprocessing.encoding.stringencoder with parameters of type Preprocessor

Constructor and Description
StringEncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices) Constructs a new instance of String Encoder preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.imputing that implement Preprocessor

Modifier and Type	Class and Description
class	ImputerPreprocessor<K,V> Preprocessing function that makes imputing.

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

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

Constructors in org.apache.ignite.ml.preprocessing.imputing with parameters of type Preprocessor

Constructor and Description
ImputerPreprocessor(Vector imputingValues, Preprocessor<K,V> basePreprocessor) Constructs a new instance of imputing preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.maxabsscaling that implement Preprocessor

Modifier and Type	Class and Description
class	MaxAbsScalerPreprocessor<K,V> The preprocessing function that makes maxabsscaling, transforms features to the scale [-1,+1].

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

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

Constructors in org.apache.ignite.ml.preprocessing.maxabsscaling with parameters of type Preprocessor

Constructor and Description
MaxAbsScalerPreprocessor(double[] maxAbs, Preprocessor<K,V> basePreprocessor) Constructs a new instance of maxabsscaling preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.minmaxscaling that implement Preprocessor

Modifier and Type	Class and Description
class	MinMaxScalerPreprocessor<K,V> Preprocessing function that makes minmaxscaling.

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

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

Constructors in org.apache.ignite.ml.preprocessing.minmaxscaling with parameters of type Preprocessor

Constructor and Description
MinMaxScalerPreprocessor(double[] min, double[] max, Preprocessor<K,V> basePreprocessor) Constructs a new instance of minmaxscaling preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.normalization that implement Preprocessor

Modifier and Type	Class and Description
class	NormalizationPreprocessor<K,V> Preprocessing function that makes normalization.

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

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

Constructors in org.apache.ignite.ml.preprocessing.normalization with parameters of type Preprocessor

Constructor and Description
NormalizationPreprocessor(int p, Preprocessor<K,V> basePreprocessor) Constructs a new instance of Normalization preprocessor.

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

Classes in org.apache.ignite.ml.preprocessing.standardscaling that implement Preprocessor

Modifier and Type	Class and Description
class	StandardScalerPreprocessor<K,V> The preprocessing function that makes standard scaling, transforms features to make mean equal to 0 and variance equal to 1.

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

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

Constructors in org.apache.ignite.ml.preprocessing.standardscaling with parameters of type Preprocessor

Constructor and Description
StandardScalerPreprocessor(double[] means, double[] sigmas, Preprocessor<K,V> basePreprocessor) Constructs a new instance of standardscaling preprocessor.

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

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

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 Preprocessor in org.apache.ignite.ml.regressions.logistic

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

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 Preprocessor in org.apache.ignite.ml.selection.cv

Fields in org.apache.ignite.ml.selection.cv declared as Preprocessor

Modifier and Type	Field and Description
protected Preprocessor<K,V>	AbstractCrossValidation.preprocessor Preprocessor.

Methods in org.apache.ignite.ml.selection.cv with parameters of type Preprocessor

Modifier and Type	Method and Description
AbstractCrossValidation<M,L,K,V>	AbstractCrossValidation.withPreprocessor(Preprocessor<K,V> preprocessor)

Uses of Preprocessor in org.apache.ignite.ml.selection.scoring.cursor

Constructors in org.apache.ignite.ml.selection.scoring.cursor with parameters of type Preprocessor

Constructor and Description
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of local truth with prediction cursor.

Uses of Preprocessor in org.apache.ignite.ml.selection.scoring.evaluator

Methods in org.apache.ignite.ml.selection.scoring.evaluator with parameters of type Preprocessor

Modifier and Type	Method and Description
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(Map<K,V> dataCache, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> RegressionMetricValues	Evaluator.evaluateRegression(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the regression metrics on the given cache.

Uses of Preprocessor in org.apache.ignite.ml.structures.partition

Constructors in org.apache.ignite.ml.structures.partition with parameters of type Preprocessor

Constructor and Description
LabeledDatasetPartitionDataBuilderOnHeap(Preprocessor<K,V> preprocessor) Constructs a new instance of SVM partition data builder.
LabelPartitionDataBuilderOnHeap(Preprocessor<K,V> preprocessor) Constructs a new instance of Label partition data builder.

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

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

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 Preprocessor in org.apache.ignite.ml.trainers

Subinterfaces of Preprocessor in org.apache.ignite.ml.trainers

Modifier and Type	Interface and Description
interface	FeatureLabelExtractor<K,V,L> Class fro extracting features and vectors from upstream.

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

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> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, int parts, Preprocessor<K,V> preprocessor) 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.
<K,V> M	DatasetTrainer.update(M mdl, Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> preprocessor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, Preprocessor<K,V> preprocessor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> M	DatasetTrainer.update(M mdl, Map<K,V> data, int parts, Preprocessor<K,V> preprocessor) Gets state of model in arguments, update 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 Preprocessor in org.apache.ignite.ml.tree

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

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 Preprocessor in org.apache.ignite.ml.tree.boosting

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

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 Preprocessor in org.apache.ignite.ml.tree.data

Constructors in org.apache.ignite.ml.tree.data with parameters of type Preprocessor

Constructor and Description
DecisionTreeDataBuilder(Preprocessor<K,V> preprocessor, boolean buildIdx) Constructs a new instance of decision tree data builder.

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

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

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.