GrayScaler

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. final def andThen[C, L](est: LabelEstimator[Image, C, L], data: PipelineDataset[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

   

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

5. final def andThen[C, L](est: LabelEstimator[Image, C, L], data: RDD[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

   

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

6. final def andThen[C, L](est: LabelEstimator[Image, C, L], data: PipelineDataset[Image], labels: RDD[L]): Pipeline[Image, C]

   

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

7. final def andThen[C, L](est: LabelEstimator[Image, C, L], data: RDD[Image], labels: RDD[L]): Pipeline[Image, C]

   

   Chains a label estimator onto the end of this pipeline, producing a new pipeline.

   Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   labels

   The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

   Definition Classes

   Chainable

8. final def andThen[C](est: Estimator[Image, C], data: PipelineDataset[Image]): Pipeline[Image, C]

   

   Chains an estimator onto the end of this pipeline, producing a new pipeline.

   Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   Definition Classes

   Chainable

9. final def andThen[C](est: Estimator[Image, C], data: RDD[Image]): Pipeline[Image, C]

   

   Chains an estimator onto the end of this pipeline, producing a new pipeline.

   Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

   est

   The estimator to chain onto the end of this pipeline

   data

   The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

   Definition Classes

   Chainable

10. final def andThen[C](next: Chainable[Image, C]): Pipeline[Image, C]

    

    Chains a pipeline onto the end of this one, producing a new pipeline.

    Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.

    next

    the pipeline to chain

    Definition Classes

    Chainable

11. def apply(in: Image): Image

    

    The application of this Transformer to a single input item.

    The application of this Transformer to a single input item. This method MUST be overridden by ML developers.

    in

    The input item to pass into this transformer

    returns

    The output value

    Definition Classes

    GrayScaler → Transformer

12. def apply(in: RDD[Image]): RDD[Image]

    

    The application of this Transformer to an RDD of input items.

    The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.

    in

    The bulk RDD input to pass into this transformer

    returns

    The bulk RDD output for the given input

    Definition Classes

    Transformer

13. final def asInstanceOf[T0]: T0

    

    Definition Classes

    Any

14. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

15. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

16. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

17. def execute(deps: Seq[Expression]): Expression

    

    Definition Classes

    TransformerOperator → Operator

18. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

19. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

20. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

21. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

22. def label: String

    

    Definition Classes

    Operator

23. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

24. final def notify(): Unit

    

    Definition Classes

    AnyRef

25. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

26. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

27. def toPipeline: Pipeline[Image, Image]

    

    A method that converts this object into a Pipeline.

    A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

    Definition Classes

    Transformer → Chainable

28. def toString(): String

    

    Definition Classes

    AnyRef → Any

29. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

30. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

31. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )