Uses of Class
org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Packages that use ModelsSequentialComposition

Package	Description
org.apache.ignite.ml.composition.combinators.sequential	Contains sequential combinators of trainers and models.

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

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

Modifier and Type	Method and Description
<K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
static <I,O> ModelsSequentialComposition<I,O,O>	ModelsSequentialComposition.ofSame(List<? extends IgniteModel<I,O>> lst, IgniteFunction<O,I> output2Input) Get sequential composition of submodels with same type.
<K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.update(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> ModelsSequentialComposition<I,O1,O2>	TrainersSequentialComposition.updateModel(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

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

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