Class/Object

org.apache.spark.mllib.rdd

RDDFunctions

Related Docs: object RDDFunctions | package rdd

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class RDDFunctions[T] extends Serializable

Machine learning specific RDD functions.

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@DeveloperApi()
Source
RDDFunctions.scala
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Serializable, Serializable, AnyRef, Any
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Instance Constructors

  1. new RDDFunctions(self: RDD[T])(implicit arg0: ClassTag[T])

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Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. def sliding(windowSize: Int): RDD[Array[T]]

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    Int)* with step = 1.

  16. def sliding(windowSize: Int, step: Int): RDD[Array[T]]

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    Returns a RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding window over them.

    Returns a RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding window over them. The ordering is first based on the partition index and then the ordering of items within each partition. This is similar to sliding in Scala collections, except that it becomes an empty RDD if the window size is greater than the total number of items. It needs to trigger a Spark job if the parent RDD has more than one partitions and the window size is greater than 1.

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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

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