Uses of Class
org.apache.ignite.ml.trainers.DatasetTrainer

Packages that use DatasetTrainer

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	Contains classes for ensemble of models implementation.
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.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.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.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.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.svm	Contains main APIs for SVM(support vector machines) algorithms.
org.apache.ignite.ml.trainers	Contains model trainers.
org.apache.ignite.ml.tree	Root package for decision trees.
org.apache.ignite.ml.tree.boosting	Contains implementation of gradient boosting on trees.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.clustering.gmm

Modifier and Type	Class and Description
class	GmmTrainer Traner for GMM model.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.clustering.kmeans

Modifier and Type	Class and Description
class	KMeansTrainer The trainer for KMeans algorithm.

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

Methods in org.apache.ignite.ml.composition that return DatasetTrainer

Modifier and Type	Method and Description
static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>	CompositionUtils.unsafeCoerce(DatasetTrainer<? extends M,L> trainer) Perform blurring of model type of given trainer to IgniteModel<I, O>, where I, O are input and output types of original model.

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

Modifier and Type	Method and Description
static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>	CompositionUtils.unsafeCoerce(DatasetTrainer<? extends M,L> trainer) Perform blurring of model type of given trainer to IgniteModel<I, O>, where I, O are input and output types of original model.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.bagging

Modifier and Type	Class and Description
class	BaggedTrainer<L> Trainer encapsulating logic of bootstrap aggregating (bagging).

Constructors in org.apache.ignite.ml.composition.bagging with parameters of type DatasetTrainer

Constructor and Description
BaggedTrainer(DatasetTrainer<? extends IgniteModel,L> tr, PredictionsAggregator aggregator, int ensembleSize, double subsampleRatio, int featuresVectorSize, int featureSubspaceDim) Construct instance of this class with given parameters.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.boosting

Modifier and Type	Class and Description
class	GDBBinaryClassifierTrainer Trainer for binary classifier using Gradient Boosting.
class	GDBRegressionTrainer Trainer for regressor using Gradient Boosting.
class	GDBTrainer Abstract Gradient Boosting trainer.

Fields in org.apache.ignite.ml.composition.boosting with type parameters of type DatasetTrainer

Modifier and Type	Field and Description
protected IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>>	GDBLearningStrategy.baseMdlTrainerBuilder Base model trainer builder.

Methods in org.apache.ignite.ml.composition.boosting that return DatasetTrainer

Modifier and Type	Method and Description
protected abstract @NotNull DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>	GDBTrainer.buildBaseModelTrainer() Returns regressor model trainer for one step of GDB.

Method parameters in org.apache.ignite.ml.composition.boosting with type arguments of type DatasetTrainer

Modifier and Type	Method and Description
GDBLearningStrategy	GDBLearningStrategy.withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> buildBaseMdlTrainer) Sets base model builder.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.combinators.parallel

Modifier and Type	Class and Description
class	TrainersParallelComposition<I,O,L> This class represents a parallel composition of trainers.

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

Modifier and Type	Method and Description
static <I,O,M extends IgniteModel<I,O>,T extends DatasetTrainer<M,L>,L> TrainersParallelComposition<I,O,L>	TrainersParallelComposition.of(List<T> trainers) Create parallel composition of trainers contained in a given list.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.combinators.sequential

Modifier and Type	Class and Description
class	TrainersSequentialComposition<I,O1,O2,L> Sequential composition of trainers.

Methods in org.apache.ignite.ml.composition.combinators.sequential that return DatasetTrainer

Modifier and Type	Method and Description
DatasetTrainer<IgniteModel<I,O2>,L>	TrainersSequentialComposition.unsafeSimplyTyped() Performs coersion of this trainer to DatasetTrainer<IgniteModel<I, O2>, L>.

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

Modifier and Type	Method and Description
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.

Constructors in org.apache.ignite.ml.composition.combinators.sequential with parameters of type DatasetTrainer

Constructor and Description
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.stacking

Modifier and Type	Class and Description
class	SimpleStackedDatasetTrainer<I,O,AM extends IgniteModel<I,O>,L> DatasetTrainer with same type of input and output of submodels.
class	StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L> DatasetTrainer encapsulating stacking technique for model training.
class	StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L> StackedDatasetTrainer with Vector as submodels input and output.

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

Modifier and Type	Method and Description
<M1 extends IgniteModel<Matrix,Matrix>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addMatrix2MatrixTrainer(DatasetTrainer<M1,L> trainer) Shortcut for adding trainer Matrix -> Matrix where this trainer is treated as Vector -> Vector, where input Vector is turned into 1 x cols Matrix and output is a first row of output Matrix.
<M1 extends IgniteModel<I,I>> SimpleStackedDatasetTrainer<I,O,AM,L>	SimpleStackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<IS,IA>> StackedDatasetTrainer<IS,IA,O,AM,L>	StackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<Vector,Vector>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<Vector,Double>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addTrainerWithDoubleOutput(DatasetTrainer<M1,L> trainer) Shortcut for adding trainer Vector -> Double where this trainer is treated as Vector -> Vector, where output Vector is constructed by wrapping double value.
StackedDatasetTrainer<IS,IA,O,AM,L>	StackedDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer) Specify aggregator trainer.
StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer) Specify aggregator trainer.
SimpleStackedDatasetTrainer<I,O,AM,L>	SimpleStackedDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer) Specify aggregator trainer.

Constructors in org.apache.ignite.ml.composition.stacking with parameters of type DatasetTrainer

Constructor and Description
SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger) Construct instance of this class.
SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger, IgniteFunction<I,I> submodelInput2AggregatingInputConverter, IgniteFunction<Vector,I> vector2SubmodelInputConverter, IgniteFunction<I,Vector> submodelOutput2VectorConverter) Construct instance of this class.
StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter) Constructs instance of this class.
StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter) Create instance of this class.
StackedVectorDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer) Constructs instance of this class.

Constructor parameters in org.apache.ignite.ml.composition.stacking with type arguments of type DatasetTrainer

Constructor and Description
StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter) Create instance of this class.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.knn

Modifier and Type	Class and Description
class	KNNTrainer<M extends KNNModel<Double>,Self extends KNNTrainer<M,Self>> KNN model trader that trains model on top of distribtued spatial indices.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.ann

Modifier and Type	Class and Description
class	ANNClassificationTrainer ANN algorithm trainer to solve multi-class classification task.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.classification

Modifier and Type	Class and Description
class	KNNClassificationTrainer KNN classification model trader that trains model on top of distribtued spatial indices.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.regression

Modifier and Type	Class and Description
class	KNNRegressionTrainer KNN regression model trader that trains model on top of distribtued spatial indices.

Uses of DatasetTrainer in org.apache.ignite.ml.multiclass

Subclasses of DatasetTrainer in org.apache.ignite.ml.multiclass

Modifier and Type	Class and Description
class	OneVsRestTrainer<M extends IgniteModel<Vector,Double>> This is a common heuristic trainer for multi-class labeled models.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.naivebayes.discrete

Modifier and Type	Class and Description
class	DiscreteNaiveBayesTrainer Trainer for the Discrete naive Bayes classification model.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.naivebayes.gaussian

Modifier and Type	Class and Description
class	GaussianNaiveBayesTrainer Trainer for the naive Bayes classification model.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.nn

Modifier and Type	Class and Description
class	MLPTrainer<P extends Serializable> Multilayer perceptron trainer based on partition based Dataset.

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

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

Modifier and Type	Method and Description
DatasetTrainer	Pipeline.getTrainer() Returns trainer.

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

Modifier and Type	Method and Description
Pipeline<K,V,C,L>	Pipeline.addTrainer(DatasetTrainer trainer) Adds a trainer.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.regressions.linear

Modifier and Type	Class and Description
class	LinearRegressionLSQRTrainer Trainer of the linear regression model based on LSQR algorithm.
class	LinearRegressionSGDTrainer<P extends Serializable> Trainer of the linear regression model based on stochastic gradient descent algorithm.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.regressions.logistic

Modifier and Type	Class and Description
class	LogisticRegressionSGDTrainer Trainer of the logistic regression model based on stochastic gradient descent algorithm.

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

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

Modifier and Type	Field and Description
protected DatasetTrainer<M,L>	AbstractCrossValidation.trainer Trainer.

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

Modifier and Type	Method and Description
AbstractCrossValidation<M,L,K,V>	AbstractCrossValidation.withTrainer(DatasetTrainer<M,L> trainer)

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.svm

Modifier and Type	Class and Description
class	SVMLinearClassificationTrainer Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.trainers

Modifier and Type	Class and Description
class	AdaptableDatasetTrainer<I,O,IW,OW,M extends IgniteModel<IW,OW>,L> Type used to adapt input and output types of wrapped DatasetTrainer.
class	MultiLabelDatasetTrainer<M extends IgniteModel> Interface for trainers that trains on dataset with multiple label per object.
class	SingleLabelDatasetTrainer<M extends IgniteModel> Interface for trainers that trains on dataset with singe label per object.

Methods in org.apache.ignite.ml.trainers that return DatasetTrainer

Modifier and Type	Method and Description
static <I,L> DatasetTrainer<IgniteModel<I,I>,L>	DatasetTrainer.identityTrainer() Returns the trainer which returns identity model.
<L1> DatasetTrainer<M,L1>	DatasetTrainer.withConvertedLabels(IgniteFunction<L1,L> new2Old) Creates DatasetTrainer with same training logic, but able to accept labels of given new type of labels.
DatasetTrainer<M,L>	DatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder) Changes learning Environment.

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

Modifier and Type	Method and Description
<O1,M1 extends IgniteModel<O,O1>> TrainersSequentialComposition<I,O,O1,L>	AdaptableDatasetTrainer.andThen(DatasetTrainer<M1,L> tr, IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMappingProducer) Create a TrainersSequentialComposition of whis trainer and specified trainer.
static <L> BaggedTrainer<L>	TrainerTransformers.makeBagged(DatasetTrainer<? extends IgniteModel,L> trainer, int ensembleSize, double subsampleRatio, PredictionsAggregator aggregator) Add bagging logic to a given trainer.
static <M extends IgniteModel<Vector,Double>,L> BaggedTrainer<L>	TrainerTransformers.makeBagged(DatasetTrainer<M,L> trainer, int ensembleSize, double subsampleRatio, int featureVectorSize, int featuresSubspaceDim, PredictionsAggregator aggregator) Add bagging logic to a given trainer.
static <I,O,M extends IgniteModel<I,O>,L> AdaptableDatasetTrainer<I,O,I,O,M,L>	AdaptableDatasetTrainer.of(DatasetTrainer<M,L> wrapped) Construct instance of this class from a given DatasetTrainer.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.tree

Modifier and Type	Class and Description
class	DecisionTree<T extends ImpurityMeasure<T>> Distributed decision tree trainer that allows to fit trees using row-partitioned dataset.
class	DecisionTreeClassificationTrainer Decision tree classifier based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.
class	DecisionTreeRegressionTrainer Decision tree regressor based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.tree.boosting

Modifier and Type	Class and Description
class	GDBBinaryClassifierOnTreesTrainer Implementation of Gradient Boosting Classifier Trainer on trees.
class	GDBRegressionOnTreesTrainer Implementation of Gradient Boosting Regression Trainer on trees.

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

Subclasses of DatasetTrainer in org.apache.ignite.ml.tree.randomforest

Modifier and Type	Class and Description
class	RandomForestClassifierTrainer Classifier trainer based on RandomForest algorithm.
class	RandomForestRegressionTrainer Regression trainer based on RandomForest algorithm.
class	RandomForestTrainer<L,S extends ImpurityComputer<BootstrappedVector,S>,T extends RandomForestTrainer<L,S,T>> Class represents a realization of Random Forest algorithm.