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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.  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.

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  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.

8.  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.

9.  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

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

   ,

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

10.  final def eq(arg0: AnyRef): Boolean

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11.  def equals(arg0: Any): Boolean

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12.  def finalize(): Unit

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13.  final def getClass(): Class[_]

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14.  def hashCode(): Int

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15.  final def isInstanceOf[T0]: Boolean

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16.  final def ne(arg0: AnyRef): Boolean

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17.  final def notify(): Unit

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18.  final def notifyAll(): Unit

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19.  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

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

    ,

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

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

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21.  def toString(): String

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22.  final def wait(): Unit

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

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24.  final def wait(arg0: Long): Unit

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