Uses of Interface
org.apache.ignite.ml.math.functions.IgniteBiFunction

Packages that use IgniteBiFunction

Package	Description
org.apache.ignite.ml.clustering.kmeans	Contains kMeans clustering algorithm.
org.apache.ignite.ml.composition	Contains classes for ensemble of models implementation.
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.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.impl.cache	Base package for cache based implementation of machine learning dataset.
org.apache.ignite.ml.dataset.impl.local	Base package for local implementation of machine learning dataset.
org.apache.ignite.ml.dataset.primitive	Package that contains basic primitives build on top of Dataset.
org.apache.ignite.ml.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.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.math.functions	Contains serializable functions for distributed code algebra.
org.apache.ignite.ml.math.primitives.matrix	Contains matrix related classes.
org.apache.ignite.ml.math.primitives.vector	Contains vector related classes.
org.apache.ignite.ml.math.primitives.vector.impl	Contains several vector implementations.
org.apache.ignite.ml.math.util	Some math utils.
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.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.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.regressions.logistic.binomial	Contains binomial logistic regression.
org.apache.ignite.ml.regressions.logistic.multiclass	Contains multi-class logistic regression.
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 IgniteBiFunction in org.apache.ignite.ml.clustering.kmeans

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

Modifier and Type	Method and Description
<K,V> KMeansModel	KMeansTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KMeansModel	KMeansTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> KMeansModel	KMeansTrainer.updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> KMeansModel	KMeansTrainer.updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of IgniteBiFunction in org.apache.ignite.ml.composition

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

Modifier and Type	Method and Description
<K,V> ModelsComposition	BaggingModelTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	BaggingModelTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	BaggingModelTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Learn new models on dataset and create new Compositions over them and already learned models.
<K,V> ModelsComposition	BaggingModelTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Learn new models on dataset and create new Compositions over them and already learned models.

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

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

Modifier and Type	Method and Description
protected <V,K> IgniteBiTuple<Double,Long>	GDBTrainer.computeInitialValue(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Compute mean value of label as first approximation.
protected <V,K> IgniteBiTuple<Double,Long>	GDBTrainer.computeInitialValue(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Compute mean value of label as first approximation.
<K,V> ModelsComposition	GDBTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	GDBTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <V,K> boolean	GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected abstract <V,K> boolean	GDBTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected abstract <V,K> boolean	GDBTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Implementation of gradient boosting iterations.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Implementation of gradient boosting iterations.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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> List<Model<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> ModelsComposition	GDBTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
abstract <K,V> ConvergenceChecker<K,V>	ConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

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

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

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

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

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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
<K,V> ConvergenceChecker<K,V>	MeanAbsValueConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

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

Constructor and Description
MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Creates an intance of MeanAbsValueConvergenceChecker.
MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Creates an intance of MeanAbsValueConvergenceChecker.

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

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

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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
<K,V> ConvergenceChecker<K,V>	MedianOfMedianConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

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

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

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

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

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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
<K,V> ConvergenceChecker<K,V>	ConvergenceCheckerStubFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

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

Constructor and Description
ConvergenceCheckerStub(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Creates an intance of ConvergenceCheckerStub.
ConvergenceCheckerStub(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Creates an intance of ConvergenceCheckerStub.

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

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

Modifier and Type	Method and Description
default <D2 extends AutoCloseable> PartitionDataBuilder<K,V,C,D2>	PartitionDataBuilder.andThen(IgniteBiFunction<D,C,D2> fun) Makes a composed partition data builder that first builds a data and then applies the specified function on the result.
default <R> R	Dataset.compute(IgniteBiFunction<D,Integer,R> map, IgniteBinaryOperator<R> reduce) Applies the specified map function to every partition data and partition index in the dataset and then reduces map results to final result by using the reduce function.
<R> R	Dataset.compute(IgniteBiFunction<D,Integer,R> map, IgniteBinaryOperator<R> reduce, R identity) Applies the specified map function to every partition data and partition index in the dataset and then reduces map results to final result by using the reduce function.
default <R> R	Dataset.computeWithCtx(IgniteBiFunction<C,D,R> map, IgniteBinaryOperator<R> reduce) Applies the specified map function to every partition data and context in the dataset and then reduces map results to final result by using the reduce function.
default <R> R	Dataset.computeWithCtx(IgniteBiFunction<C,D,R> map, IgniteBinaryOperator<R> reduce, R identity) Applies the specified map function to every partition data and context in the dataset and then reduces map results to final result by using the reduce function.
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,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and 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,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and 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,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of local SimpleDataset using the specified partCtxBuilder and 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(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,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, 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(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,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, 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.
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.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of local SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of local SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

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

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

Constructor and Description
BootstrappedDatasetBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, int samplesCnt, double subsampleSize) Creates an instance of BootstrappedDatasetBuilder.
BootstrappedDatasetBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, int samplesCnt, double subsampleSize) Creates an instance of BootstrappedDatasetBuilder.

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

Methods in org.apache.ignite.ml.dataset.impl.cache with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
<R> R	CacheBasedDataset.compute(IgniteBiFunction<D,Integer,R> map, IgniteBinaryOperator<R> reduce, R identity) Applies the specified map function to every partition data and partition index in the dataset and then reduces map results to final result by using the reduce function.

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

Methods in org.apache.ignite.ml.dataset.impl.local with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
<R> R	LocalDataset.compute(IgniteBiFunction<D,Integer,R> map, IgniteBinaryOperator<R> reduce, R identity) Applies the specified map function to every partition data and partition index in the dataset and then reduces map results to final result by using the reduce function.

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

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

Modifier and Type	Method and Description
<R> R	DatasetWrapper.compute(IgniteBiFunction<D,Integer,R> map, IgniteBinaryOperator<R> reduce, R identity) Applies the specified map function to every partition data and partition index in the dataset and then reduces map results to final result by using the reduce function.

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

Constructor and Description
FeatureMatrixWithLabelsOnHeapDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Constructs a new instance of decision tree data builder.
FeatureMatrixWithLabelsOnHeapDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Constructs a new instance of decision tree data builder.

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

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

Constructor and Description
SimpleDatasetDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor) Construct a new instance of partition data builder that makes SimpleDatasetData.
SimpleLabeledDatasetDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Constructs a new instance of partition data builder that makes SimpleLabeledDatasetData.
SimpleLabeledDatasetDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Constructs a new instance of partition data builder that makes SimpleLabeledDatasetData.

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

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

Modifier and Type	Method and Description
static <K,V> @Nullable Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>	KNNUtils.buildDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Builds dataset.
static <K,V> @Nullable Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>	KNNUtils.buildDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Builds dataset.

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

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

Modifier and Type	Method and Description
<K,V> ANNClassificationModel	ANNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ANNClassificationModel	ANNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ANNClassificationModel	ANNClassificationTrainer.updateModel(ANNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> ANNClassificationModel	ANNClassificationTrainer.updateModel(ANNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
<K,V> KNNClassificationModel	KNNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNClassificationModel	KNNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNClassificationModel	KNNClassificationTrainer.updateModel(KNNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> KNNClassificationModel	KNNClassificationTrainer.updateModel(KNNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
<K,V> KNNRegressionModel	KNNRegressionTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNRegressionModel	KNNRegressionTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNRegressionModel	KNNRegressionTrainer.updateModel(KNNRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> KNNRegressionModel	KNNRegressionTrainer.updateModel(KNNRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of IgniteBiFunction in org.apache.ignite.ml.math.functions

Subinterfaces of IgniteBiFunction in org.apache.ignite.ml.math.functions

Modifier and Type	Interface and Description
interface	IntIntToDoubleFunction Getters functions for matrices.

Fields in org.apache.ignite.ml.math.functions declared as IgniteBiFunction

Modifier and Type	Field and Description
static IgniteBiFunction<Double,Double,Double>	Functions.COMPARE Function that returns {@code a < b ?
static IgniteBiFunction<Double,Double,Double>	Functions.LG Function that returns Math.log(a) / Math.log(b).
static IgniteBiFunction<Double,Double,Double>	Functions.MAX_ABS Function that returns max(abs(a), abs(b)).
static IgniteBiFunction<Double,Double,Double>	Functions.MIN Function that returns min(a, b).
static IgniteBiFunction<Double,Double,Double>	Functions.MIN_ABS Function that returns min(abs(a), abs(b)).
static IgniteBiFunction<Double,Double,Double>	Functions.MINUS Function that returns a - b.
static IgniteBiFunction<Double,Double,Double>	Functions.MINUS_ABS Function that returns abs(a - b).
static IgniteBiFunction<Double,Double,Double>	Functions.MINUS_SQUARED Function that returns (a - b) * (a - b)
static IgniteBiFunction<Double,Double,Double>	Functions.MOD Function that returns a % b.
static IgniteBiFunction<Double,Double,Double>	Functions.MULT Function that returns a * b.
static IgniteBiFunction<Double,Double,Double>	Functions.PLUS Function that returns a + b.
static IgniteBiFunction<Double,Double,Double>	Functions.PLUS_ABS Function that returns Math.abs(a) + Math.abs(b).

Methods in org.apache.ignite.ml.math.functions that return IgniteBiFunction

Modifier and Type	Method and Description
default <V> IgniteBiFunction<T,U,V>	IgniteBiFunction.andThen(IgniteFunction<? super R,? extends V> after)
static IgniteBiFunction<Double,Double,Double>	Functions.minusMult(double constant) Function that returns a - b*constant.
static <A,B,C> IgniteBiFunction<A,B,IgniteSupplier<C>>	Functions.outputSupplier(IgniteBiFunction<A,B,C> f) Transform function of form (a, b) -> c into (a, b) - () -> c.
static IgniteBiFunction<Double,Double,Double>	Functions.plusMult(double constant) Function that returns a + b*constant.

Methods in org.apache.ignite.ml.math.functions with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
static <A,B,C> IgniteCurriedBiFunction<A,B,C>	Functions.curry(IgniteBiFunction<A,B,C> f) Transform bi-function of the form (a, b) -> c into a function of form a -> (b -> c).
static <A,B,C> IgniteBiFunction<A,B,IgniteSupplier<C>>	Functions.outputSupplier(IgniteBiFunction<A,B,C> f) Transform function of form (a, b) -> c into (a, b) - () -> c.

Uses of IgniteBiFunction in org.apache.ignite.ml.math.primitives.matrix

Methods in org.apache.ignite.ml.math.primitives.matrix with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
<T> T	AbstractMatrix.foldMap(IgniteBiFunction<T,Double,T> foldFun, IgniteDoubleFunction<Double> mapFun, T zeroVal) Folds this matrix into a single value.
<T> T	Matrix.foldMap(IgniteBiFunction<T,Double,T> foldFun, IgniteDoubleFunction<Double> mapFun, T zeroVal) Folds this matrix into a single value.
Matrix	AbstractMatrix.map(Matrix mtx, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this matrix through a given function.
Matrix	Matrix.map(Matrix mtx, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this matrix through a given function.

Uses of IgniteBiFunction in org.apache.ignite.ml.math.primitives.vector

Methods in org.apache.ignite.ml.math.primitives.vector with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
<T> T	AbstractVector.foldMap(IgniteBiFunction<T,Double,T> foldFun, IgniteDoubleFunction<Double> mapFun, T zeroVal) Folds this vector into a single value.
<T> T	Vector.foldMap(IgniteBiFunction<T,Double,T> foldFun, IgniteDoubleFunction<Double> mapFun, T zeroVal) Folds this vector into a single value.
<T> T	AbstractVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
<T> T	AbstractVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
<T> T	Vector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
<T> T	Vector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
Vector	AbstractVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	Vector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	AbstractVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
Vector	Vector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.
static Vector	VectorUtils.zipWith(Vector v1, Vector v2, IgniteBiFunction<Double,Double,Double> f) Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

Uses of IgniteBiFunction in org.apache.ignite.ml.math.primitives.vector.impl

Methods in org.apache.ignite.ml.math.primitives.vector.impl with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
<T> T	DelegatingVector.foldMap(IgniteBiFunction<T,Double,T> foldFun, IgniteDoubleFunction<Double> mapFun, T zeroVal) Folds this vector into a single value.
<T> T	DelegatingVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
<T> T	DelegatingVector.foldMap(Vector vec, IgniteBiFunction<T,Double,T> foldFun, IgniteBiFunction<Double,Double,Double> combFun, T zeroVal) Combines & maps two vector and folds them into a single value.
Vector	DelegatingVector.map(IgniteBiFunction<Double,Double,Double> fun, double y) Maps all elements of this vector by applying given function to each element with a constant second parameter y.
Vector	DelegatingVector.map(Vector vec, IgniteBiFunction<Double,Double,Double> fun) Maps all values in this vector through a given function.

Uses of IgniteBiFunction in org.apache.ignite.ml.math.util

Methods in org.apache.ignite.ml.math.util with parameters of type IgniteBiFunction

Modifier and Type	Method and Description
static Vector	MatrixUtil.zipFoldByColumns(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by column-by-column with specified function.
static Vector	MatrixUtil.zipFoldByRows(Matrix mtx1, Matrix mtx2, IgniteBiFunction<Vector,Vector,Double> fun) Zips two matrices by row-by-row with specified function.

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

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

Modifier and Type	Method and Description
<K,V> MultilayerPerceptron	MLPTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Trains model based on the specified data.
<K,V> MultilayerPerceptron	MLPTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Trains model based on the specified data.
protected <K,V> MultilayerPerceptron	MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> MultilayerPerceptron	MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
IgniteBiFunction<K,V,Vector>	PipelineMdl.getFeatureExtractor()
IgniteBiFunction<K,V,Double>	PipelineMdl.getLabelExtractor()

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

Modifier and Type	Method and Description
Pipeline<K,V,R>	Pipeline.addFeatureExtractor(IgniteBiFunction<K,V,R> featureExtractor) Adds feature extractor as a zero stage.
Pipeline<K,V,R>	Pipeline.addLabelExtractor(IgniteBiFunction<K,V,Double> lbExtractor) Adds a label extractor for the produced model.
PipelineMdl<K,V>	PipelineMdl.withFeatureExtractor(IgniteBiFunction featureExtractor)
PipelineMdl<K,V>	PipelineMdl.withLabelExtractor(IgniteBiFunction<K,V,Double> lbExtractor)

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

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

Modifier and Type	Method and Description
IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.
default IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.
default IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(Map<K,V> data, int parts, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.

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

Modifier and Type	Method and Description
IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.
default IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.
default IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(Map<K,V> data, int parts, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.

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

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

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 IgniteBiFunction

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

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

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

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

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

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 IgniteBiFunction

Modifier and Type	Field and Description
protected IgniteBiFunction<K,V,Object[]>	EncoderPreprocessor.basePreprocessor Base preprocessor.

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

Modifier and Type	Method and Description
EncoderPreprocessor<K,V>	EncoderTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Object[]> basePreprocessor) Fits preprocessor.

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

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

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

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

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 IgniteBiFunction

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

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

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

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 IgniteBiFunction

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

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

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

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 IgniteBiFunction

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

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

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

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

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

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 IgniteBiFunction

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

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

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

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

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

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 IgniteBiFunction

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

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

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

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

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

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 IgniteBiFunction

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

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

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

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

Classes in org.apache.ignite.ml.regressions.linear that implement IgniteBiFunction

Modifier and Type	Class and Description
class	FeatureExtractorWrapper<K,V> Feature extractor wrapper that adds additional column filled by 1.

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

Modifier and Type	Method and Description
<K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> LinearRegressionModel	LinearRegressionSGDTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> LinearRegressionModel	LinearRegressionSGDTrainer.updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of IgniteBiFunction in org.apache.ignite.ml.regressions.logistic.binomial

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

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of IgniteBiFunction in org.apache.ignite.ml.regressions.logistic.multiclass

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

Modifier and Type	Method and Description
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.updateModel(LogRegressionMultiClassModel newMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.updateModel(LogRegressionMultiClassModel newMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
CrossValidationResult	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv, ParamGrid paramGrid) Computes cross-validated metrics with a passed parameter grid.
CrossValidationResult	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv, ParamGrid paramGrid) Computes cross-validated metrics with a passed parameter grid.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Ignite ignite, IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, UniformMapper<K,V> mapper, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.
double[]	CrossValidation.score(DatasetTrainer<M,L> trainer, Metric<L> scoreCalculator, Map<K,V> upstreamMap, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, int cv) Computes cross-validated metrics.

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

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

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

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

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

Modifier and Type	Method and Description
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, Model<Vector,L> mdl, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor, Accuracy<L> metric) Computes the given metric on the given cache.

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

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

Constructor and Description
LabeledDatasetPartitionDataBuilderOnHeap(IgniteBiFunction<K,V,Vector> xExtractor, IgniteBiFunction<K,V,Double> yExtractor) Constructs a new instance of SVM partition data builder.
LabeledDatasetPartitionDataBuilderOnHeap(IgniteBiFunction<K,V,Vector> xExtractor, IgniteBiFunction<K,V,Double> yExtractor) Constructs a new instance of SVM partition data builder.
LabelPartitionDataBuilderOnHeap(IgniteBiFunction<K,V,Double> yExtractor) Constructs a new instance of Label partition data builder.

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

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

Modifier and Type	Method and Description
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.updateModel(SVMLinearBinaryClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.updateModel(SVMLinearBinaryClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.updateModel(SVMLinearMultiClassClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.updateModel(SVMLinearMultiClassClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
abstract <K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
abstract <K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.fit(Map<K,V> data, int parts, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.update(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiPredicate<K,V> filter, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) 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, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected abstract <K,V> M	DatasetTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected abstract <K,V> M	DatasetTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

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

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

Modifier and Type	Method and Description
<K,V> DecisionTreeNode	DecisionTree.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> DecisionTreeNode	DecisionTree.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> DecisionTreeNode	DecisionTree.updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains new model based on dataset because there is no valid approach to update decision trees.
<K,V> DecisionTreeNode	DecisionTree.updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains new model based on dataset because there is no valid approach to update decision trees.

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

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

Modifier and Type	Method and Description
<K,V> List<Model<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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> List<Model<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) 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 IgniteBiFunction in org.apache.ignite.ml.tree.data

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

Constructor and Description
DecisionTreeDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, boolean buildIdx) Constructs a new instance of decision tree data builder.
DecisionTreeDataBuilder(IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, boolean buildIdx) Constructs a new instance of decision tree data builder.

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

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

Modifier and Type	Method and Description
<K,V> ModelsComposition	RandomForestTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	RandomForestTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.