org.apache.mahout.math.drm

DrmLikeOps

class DrmLikeOps[K] extends AnyRef

Common Drm ops

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Instance Constructors

  1. new DrmLikeOps(drm: DrmLike[K])

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  6. def apply(f: (Double) ⇒ Double, evalZeros: T = AutoBooleanEnum.AUTO): OpAewUnaryFunc[K]

    Apply a function element-wise.

    Apply a function element-wise.

    f

    element-wise function

    evalZeros

    Do we have to process zero elements? true, false, auto: if auto, we will test the supplied function for f(0) != 0, and depending on the result, will decide if we want evaluation for zero elements. WARNING: the AUTO setting may not always work correctly for functions that are meant to run in a specific backend context, or non-deterministic functions, such as {-1,0,1} random generators.

    returns

    new DRM with the element-wise function applied.

  7. def apply(rowRange: Range, colRange: Range): DrmLike[K]

    Slicing the DRM.

    Slicing the DRM. Should eventually work just like in-core drm (e.g. A(0 until 5, 5 until 15)).<P>

    The all-range is denoted by '::', e.g.: A(::, 0 until 5).<P>

    Row range is currently unsupported except for the all-range. When it will be fully supported, the input must be Int-keyed, i.e. of DrmLike[Int] type for non-all-range specifications.

    rowRange

    Row range. This must be '::' (all-range) unless matrix rows are keyed by Int key.

    colRange

    col range. Must be a sub-range of 0 until ncol. '::' denotes all-range.

  8. final def asInstanceOf[T0]: T0

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  9. def clone(): AnyRef

    Attributes
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    @throws( ... )
  10. val drm: DrmLike[K]

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    protected[org.apache.mahout.math.drm]
  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def mapBlock[R](ncol: Int = 1, identicallyPartitioned: Boolean = true)(bmf: ((Array[K], Matrix)) ⇒ (Array[R], Matrix))(implicit arg0: ClassTag[R]): DrmLike[R]

    Map matrix block-wise vertically.

    Map matrix block-wise vertically. Blocks of the new matrix can be modified original block matrices; or they could be completely new matrices with new keyset. In the latter case, output matrix width must be specified with ncol parameter.<P>

    New block heights must be of the same height as the original geometry.<P>

    R
    ncol

    new matrix' width (only needed if width changes).

    bmf
    returns

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def par(min: Int = 1, exact: Int = 1, auto: Boolean = false): OpPar[K]

    Parallelism adjustments.

    Parallelism adjustments. <P/>

    Change only one of parameters from default value to choose new parallelism adjustment strategy. <P/>

    E.g. use

      drmA.par(auto = true)
    
    to use automatic parallelism adjustment. <P/>

    Parallelism here in API is fairly abstract concept, and actual value interpretation is left for a particular backend strategy. However, it is usually equivalent to number of map tasks or data splits. <P/>

    min

    If changed from default, ensures the product has at least that much parallelism.

    exact

    if changed from default, ensures the pipeline product has exactly that much parallelism.

    auto

    If changed from default, engine-specific automatic parallelism adjustment strategy is applied.

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

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  23. def toString(): String

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  24. final def wait(): Unit

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

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