spark.util

Distribution

class Distribution extends AnyRef

Util for getting some stats from a small sample of numeric values, with some handy summary functions.

Entirely in memory, not intended as a good way to compute stats over large data sets.

Assumes you are giving it a non-empty set of data

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Instance Constructors

  1. new Distribution(data: Traversable[Double])

  2. new Distribution(data: Array[Double], startIdx: Int, endIdx: Int)

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. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[lang]
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    @throws()

  8. val data: Array[Double]

  9. val defaultProbabilities: Array[Double]

  10. val endIdx: Int

  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(): java.lang.Class[_]

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  15. def getQuantiles(probabilities: Traversable[Double] = defaultProbabilities): IndexedSeq[Double]

    Get the value of the distribution at the given probabilities.

    Get the value of the distribution at the given probabilities. Probabilities should be given from 0 to 1

    probabilities

  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. val length: Int

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

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def showQuantiles(out: PrintStream = System.out): Unit

  23. val startIdx: Int

  24. def statCounter: StatCounter

  25. def summary(out: PrintStream = System.out): Unit

    print a summary of this distribution to the given PrintStream.

    print a summary of this distribution to the given PrintStream.

    out

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

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  27. def toString(): String

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  28. final def wait(): Unit

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

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

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