## Uses of Interface
org.apache.ignite.ml.math.functions.IgniteBinaryOperator

- Packages that use IgniteBinaryOperator
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.stacking | Contains classes used for training with stacking technique.
  org.apache.ignite.ml.dataset | Base package for machine learning dataset classes.
  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`.

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

    Methods in org.apache.ignite.ml.composition.stacking with parameters of type IgniteBinaryOperator
    Modifier and Type | Method and Description
    --- | ---
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<I> merger)` Specify binary operator used to merge submodels outputs to one.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | StackedDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<IA> merger)` Specify binary operator used to merge submodels outputs to one.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<Vector> merger)` Specify binary operator used to merge submodels outputs to one.

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

  - ### Uses of IgniteBinaryOperator in org.apache.ignite.ml.dataset

    Methods in org.apache.ignite.ml.dataset with parameters of type IgniteBinaryOperator
    Modifier and Type | Method and Description
    --- | ---
    `default <R> R` | Dataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | Dataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `default <R> R` | Dataset.`compute(IgniteFunction<D,R> map, IgniteBinaryOperator<R> reduce)` Applies the specified `map` function to every partition `data` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `default <R> R` | Dataset.`compute(IgniteFunction<D,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` 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.
    `default <R> R` | Dataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce)` Applies the specified `map` function to every partition `data`, `context` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | Dataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

  - ### Uses of IgniteBinaryOperator in org.apache.ignite.ml.dataset.impl.cache

    Methods in org.apache.ignite.ml.dataset.impl.cache with parameters of type IgniteBinaryOperator
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | CacheBasedDataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | CacheBasedDataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

  - ### Uses of IgniteBinaryOperator in org.apache.ignite.ml.dataset.impl.local

    Methods in org.apache.ignite.ml.dataset.impl.local with parameters of type IgniteBinaryOperator
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | LocalDataset.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | LocalDataset.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

  - ### Uses of IgniteBinaryOperator in org.apache.ignite.ml.dataset.primitive

    Methods in org.apache.ignite.ml.dataset.primitive with parameters of type IgniteBinaryOperator
    Modifier and Type | Method and Description
    --- | ---
    `<R> R` | DatasetWrapper.`compute(IgniteBiFunction<D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data` and `LearningEnvironment` in the dataset and then reduces `map` results to final result by using the `reduce` function.
    `<R> R` | DatasetWrapper.`computeWithCtx(IgniteTriFunction<C,D,LearningEnvironment,R> map, IgniteBinaryOperator<R> reduce, R identity)` Applies the specified `map` function to every partition `data`, `context` and partition index in the dataset and then reduces `map` results to final result by using the `reduce` function.

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Release Date | : April 25 2023