class Statistic extends Error

The Statistic class is used to collect values and compute sample statistics on them (e.g., waiting time). Contrast with TimeStatistic defined below.

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Error, AnyRef, Any
Known Subclasses
TimeStatistic
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new Statistic(name: String = "stat", unbiased: Boolean = false)

    name

    the name for this statistic (e.g., 'waitingTime')

    unbiased

    whether the estimators are restricted to be unbiased

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
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  5. def clone(): AnyRef
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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  9. final def getClass(): Class[_ <: AnyRef]
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    @native() @HotSpotIntrinsicCandidate()
  10. def hashCode(): Int
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  11. def interval(p: Double = .95): Double

    Compute the confidence interval half-width for the given confidence level.

    Compute the confidence interval half-width for the given confidence level.

    p

    the confidence level

  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. def ma: Double

    Compute/estimate the mean absolue value (ma), e.g., Mean Absolute Error (MAE).

  14. def max: Double

    Return the maximum value in sample.

  15. var maxX: Double

    The maximum sample value

    The maximum sample value

    Attributes
    protected
  16. def mean: Double

    Compute/estimate the sample mean.

  17. def min: Double

    Return the minimum value in sample.

  18. var minX: Double

    The minimum sample value

    The minimum sample value

    Attributes
    protected
  19. def ms: Double

    Compute/estimate the mean square (ms), e.g., Mean Square Error (MSE).

  20. var n: Int

    The number of samples

    The number of samples

    Attributes
    protected
  21. val name: String
  22. def nd: Double

    Return the number of samples as a double.

  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  25. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  26. def num: Int

    Return the number of samples.

  27. def reset(): Unit
  28. def rms: Double

    Compute/estimate the root mean square (rms), e.g., Root Mean Square Error (RMSE).

  29. def show: String

    Show the values of this collector's accumulators.

  30. def statRow: Array[Any]

    Return the summary statistics as a row/Array.

  31. def stddev: Double

    Compute/estimate the sample standard deviation.

  32. var sum: Double

    Sum of the sample values

    Sum of the sample values

    Attributes
    protected
  33. var sumAb: Double

    Sum of the sample absolute values

    Sum of the sample absolute values

    Attributes
    protected
  34. var sumSq: Double

    Sum of the sample values squared

    Sum of the sample values squared

    Attributes
    protected
  35. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  36. def tally(x: Double): Unit

    Tally the next value and update accumulators.

    Tally the next value and update accumulators.

    x

    the value to tally

  37. def toString(): String

    Generate a row of statistical results as a string.

    Generate a row of statistical results as a string.

    Definition Classes
    Statistic → AnyRef → Any
  38. def variance: Double

    Compute/estimate the sample variance.

    Compute/estimate the sample variance. The denominator is one less for unbiased (n-1) vs. maximum likelihood (n) estimators. Also use n for population variance.

  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  41. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

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