Object

org.scalameter.utils

Statistics

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object Statistics

Standard statistics utilities.

Note: significance level alpha is equal to 1 - confidenceLevel. If you want to be sure that 2 sets of measurements do not differ with 90 percent probability, then the significance level alpha should be set to 0.1. In this example, the confidence level is 0.9, and the significance level is 0.1.

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  1. case class ANOVAFTest(alternatives: Seq[Seq[Double]], alpha: Double) extends Test with Product with Serializable

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    ANOVA separates the total variation in a set of measurements into a component due to random fluctuations in the measurements and a component due to the actual differences among the alternatives.

    ANOVA separates the total variation in a set of measurements into a component due to random fluctuations in the measurements and a component due to the actual differences among the alternatives.

    If the variation between the alternatives is larger than the variation within each alternative, then it can be concluded that there is a statistically significant difference between the alternatives.

    For more information see: Andy Georges, Dries Buytaert, Lieven Eeckhout - Statistically Rigorous Java Performance Evaluation

  2. case class CoVTest(measurements: Seq[Double], threshold: Double) extends Test with Product with Serializable

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    Compares the coefficient of variance to some threshold value.

    Compares the coefficient of variance to some threshold value.

    This heuristic can be used to detect if the measurement has stabilized.

  3. case class ConfidenceIntervalTest(strict: Boolean, alt1: Seq[Double], alt2: Seq[Double], alpha: Double) extends Standard2WayTest with Product with Serializable

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    Compares two alternative sets of measurements given a significance level alpha.

    Compares two alternative sets of measurements given a significance level alpha.

    strict

    if true, the confidence interval test is strict - the confidence interval overlap of the alternatives will not be additionally compared

    returns

    returns true if there is no statistical difference for s.l. alpha

  4. case class OverlapTest(alt1: Seq[Double], alt2: Seq[Double], alpha: Double, noiseMagnitude: Double) extends Standard2WayTest with Product with Serializable

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    Computes the confidence interval of the two alternatives.

    Computes the confidence interval of the two alternatives. Passes if the confidence intervals overlap at the given significance level alpha.

    Applies relative noise before doing the actual overlap test - the relative noise may increase the confidence interval further, but it will not shrink it.

  5. abstract class Standard2WayTest extends Test

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  6. trait Test extends AnyRef

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Value Members

  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. def CoV(measurements: Seq[Double]): Double

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  5. def SSA(alternatives: Seq[Seq[Double]]): Double

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    Computes sum-of-squares due to differences between alternatives.

  6. def SSE(alternatives: Seq[Seq[Double]]): Double

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    Computes sum-of-squares due to errors in measurements.

  7. final def asInstanceOf[T0]: T0

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  8. def clamp(x: Double, below: Double, above: Double): Double

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  9. def clone(): AnyRef

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  10. def confidenceInterval(seq: Seq[Double], alpha: Double): (Double, Double)

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    Let Y = (Y_1, ..., Y_n) data resulting from a parametric law F of scalar parameter θ.

    Let Y = (Y_1, ..., Y_n) data resulting from a parametric law F of scalar parameter θ. A confidence interval (B_i, B_s) is a statistic in the form of an interval containing θ with a specified probability.

  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 mean(seq: Seq[Double]): Double

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    Computes the mean of the sequence of measurements.

  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 stdev(seq: Seq[Double]): Double

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    The sample standard sample deviation.

    The sample standard sample deviation. It is the square root of S², unbiased estimator for the variance.

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

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  23. implicit def test2boolean(t: Test): Boolean

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  24. def toString(): String

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  25. final def wait(): Unit

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

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

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