TDigest

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

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6. val K: Double

   The t-digest algorithm will re-cluster itself whenever its number of clusters exceeds (K/delta).

   The t-digest algorithm will re-cluster itself whenever its number of clusters exceeds (K/delta). This value is set such that the threshold is about 10x the heuristically expected number of clusters for the user-specified delta value. Generally the number of clusters will only trigger the corresponding re-clustering threshold when data are being presented in a non-random order.

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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   protected[java.lang]

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9. def combine(ltd: TDigest, rtd: TDigest, delta: Double = deltaDefault, maxDiscrete: Int = 0): TDigest

   Combine two t-digests to yield a new digest

   Combine two t-digests to yield a new digest

   ltd

   the left-hand t-digest operand

   rtd

   the right hand t-digest

   delta

   a sketch resolution parameter.

   maxDiscrete

   sketch in discrete distribution mode up to this number of unique values. Defaults to zero; normal continuous mode.

   returns

   the sum of left and right digests, defined as their aggregation

   Note

   This operation satisfies a Semigroup law, with the caveat that it is only "statistically" associative: d1++(d2++d3) will be statistically similar to (d1++d2)++d3, but rarely identical.

10. val deltaDefault: Double

    Default value for a t-digest delta parameter.

    Default value for a t-digest delta parameter. The number of clusters varies, roughly, as about (50/delta), when data are presented in random order (it may grow larger if data are not presented randomly). The default corresponds to an expected number of clusters of about 100.

11. def empty(delta: Double = deltaDefault, maxDiscrete: Int = 0): TDigest

    Obtain an empty t-digest

    Obtain an empty t-digest

    delta

    a sketch resolution parameter.

    maxDiscrete

    sketch in discrete distribution mode up to this number of unique values. Defaults to zero; normal continuous mode.

    Note

    The expected number of clusters will vary (roughly) as (50/delta)

    ,

    Smaller values of delta yield sketches with more clusters, and higher resolution

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

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13. def equals(arg0: Any): Boolean

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14. def finalize(): Unit

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

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16. def hashCode(): Int

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17. final def isInstanceOf[T0]: Boolean

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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 sketch[N](data: TraversableOnce[N], delta: Double = deltaDefault, maxDiscrete: Int = 0)(implicit num: Numeric[N]): TDigest

    Sketch some data with a t-digest

    Sketch some data with a t-digest

    data

    The data elements to sketch

    delta

    The sketch resolution parameter.

    maxDiscrete

    sketch in discrete distribution mode up to this number of unique values. Defaults to zero; normal continuous mode.

    returns

    A t-digest sketch of the input data

    Note

    The expected number of clusters will vary (roughly) as (50/delta)

    ,

    Smaller values of delta yield sketches with more clusters, and higher resolution

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

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

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