criterion-0.1.3: Robust, reliable performance measurement and analysis Source code Contents Index

Criterion.Analysis Portability GHC Stability experimental Maintainer bos@serpentine.com

Description

Analysis code for benchmarks.

Synopsis

data Outliers = Outliers { samplesSeen :: !Int64 lowSevere :: !Int64 lowMild :: !Int64 highMild :: !Int64 highSevere :: !Int64 } data OutlierVariance = Unaffected | Slight | Moderate | Severe analyseMean :: Sample -> Int -> ConfigM Double countOutliers :: Outliers -> Int64 classifyOutliers :: Sample -> Outliers noteOutliers :: Outliers -> ConfigM () outlierVariance :: Estimate -> Estimate -> Double -> (OutlierVariance, Double)

Documentation

data Outliers Source

Outliers from sample data, calculated using the boxplot technique. Constructors Outliers samplesSeen :: !Int64 lowSevere :: !Int64 More than 3 times the IQR below the first quartile. lowMild :: !Int64 Between 1.5 and 3 times the IQR below the first quartile. highMild :: !Int64 Between 1.5 and 3 times the IQR above the third quartile. highSevere :: !Int64 More than 3 times the IQR above the third quartile. Instances Eq Outliers Read Outliers Show Outliers Monoid Outliers

data OutlierVariance Source

A description of the extent to which outliers in the sample data affect the sample mean and standard deviation. Constructors Unaffected Less than 1% effect. Slight Between 1% and 10%. Moderate Between 10% and 50%. Severe Above 50% (i.e. measurements are useless). Instances Eq OutlierVariance Ord OutlierVariance Show OutlierVariance

analyseMean Source

:: Sample -> Int Number of iterations used to compute the sample. -> ConfigM Double Display the mean of a Sample, and characterise the outliers present in the sample.

countOutliers :: Outliers -> Int64 Source

Count the total number of outliers in a sample.

classifyOutliers :: Sample -> Outliers Source

Classify outliers in a data set, using the boxplot technique.

noteOutliers :: Outliers -> ConfigM () Source

Display a report of the Outliers present in a Sample.

outlierVariance Source

:: Estimate Bootstrap estimate of sample mean. -> Estimate Bootstrap estimate of sample standard deviation. -> Double Number of original iterations. -> (OutlierVariance, Double) Compute the extent to which outliers in the sample data affect the sample mean and standard deviation.

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