## Uses of Class
org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

- Packages that use StackedVectorDatasetTrainer
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.stacking | Contains classes used for training with stacking technique.

- - ### Uses of StackedVectorDatasetTrainer in org.apache.ignite.ml.composition.stacking

    Methods in org.apache.ignite.ml.composition.stacking that return StackedVectorDatasetTrainer
    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<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.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<Vector> merger)` Specify binary operator used to merge submodels outputs to one.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.
    `<L1> StackedVectorDatasetTrainer<O,AM,L1>` | StackedVectorDatasetTrainer.`withConvertedLabels(IgniteFunction<L1,L> new2Old)` Creates `DatasetTrainer` with same training logic, but able to accept labels of given new type of labels.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)` Changes learning Environment.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withOriginalFeaturesDropped()` Drop original features during training and inference.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withOriginalFeaturesKept()` Keep original features using `IgniteFunction.identity()` as submodelInput2AggregatingInputConverter.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withOriginalFeaturesKept(IgniteFunction<Vector,Vector> submodelInput2AggregatingInputConverter)` Keep original features during training and propagate submodels input to aggregator during inference using given function.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withSubmodelOutput2VectorConverter(IgniteFunction<Vector,Vector> submodelOutput2VectorConverter)` Set function used for conversion of submodel output to `Vector`.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withVector2SubmodelInputConverter(IgniteFunction<Vector,Vector> vector2SubmodelInputConverter)` Set function used for conversion of `Vector` to submodel input.

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Ignite Database and Caching Platform | : ver. 2.13.0
Release Date | : April 20 2022