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

Packages that use AdaptableDatasetModel

Package	Description
org.apache.ignite.ml.trainers	Contains model trainers.

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

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

Modifier and Type	Method and Description
<I1> AdaptableDatasetModel<I1,O,IW,OW,M>	AdaptableDatasetModel.andBefore(IgniteFunction<I1,I> before) Create new AdaptableDatasetModel which is a composition of the form thisMdl . before.
<O1> AdaptableDatasetModel<I,O1,IW,OW,M>	AdaptableDatasetModel.andThen(IgniteModel<O,O1> after) Get a composition model of the form x -> after(mdl(x)).
<K,V> AdaptableDatasetModel<I,O,IW,OW,M>	AdaptableDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> AdaptableDatasetModel<I,O,IW,OW,M>	AdaptableDatasetTrainer.updateModel(AdaptableDatasetModel<I,O,IW,OW,M> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
<M1 extends IgniteModel<IW,OW>> AdaptableDatasetModel<I,O,IW,OW,M1>	AdaptableDatasetModel.withInnerModel(M1 mdl) Create new instance of this class with changed inner model.

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

Modifier and Type	Method and Description
boolean	AdaptableDatasetTrainer.isUpdateable(AdaptableDatasetModel<I,O,IW,OW,M> mdl)
protected <K,V> AdaptableDatasetModel<I,O,IW,OW,M>	AdaptableDatasetTrainer.updateModel(AdaptableDatasetModel<I,O,IW,OW,M> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Method parameters in org.apache.ignite.ml.trainers with type arguments of type AdaptableDatasetModel

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.