criterion-0.8.0.2: Robust, reliable performance measurement and analysis

PortabilityGHC
Stabilityexperimental
Maintainerbos@serpentine.com
Safe HaskellNone

Criterion.Analysis

Description

Analysis code for benchmarks.

Synopsis

Documentation

data Outliers Source

Outliers from sample data, calculated using the boxplot technique.

Constructors

Outliers 

Fields

samplesSeen :: !Int64
 
lowSevere :: !Int64

More than 3 times the interquartile range (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.

data OutlierEffect 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).

data OutlierVariance Source

Analysis of the extent to which outliers in a sample affect its standard deviation (and to some extent, its mean).

Constructors

OutlierVariance 

Fields

ovEffect :: OutlierEffect

Qualitative description of effect.

ovDesc :: String

Brief textual description of effect.

ovFraction :: Double

Quantitative description of effect (a fraction between 0 and 1).

analyseSampleSource

Arguments

:: Double

Confidence interval (between 0 and 1).

-> Sample

Sample data.

-> Int

Number of resamples to perform when bootstrapping.

-> IO SampleAnalysis 

Perform a bootstrap analysis of a non-parametric sample.

scaleSource

Arguments

:: Double

Value to multiply by.

-> SampleAnalysis 
-> SampleAnalysis 

Multiply the Estimates in an analysis by the given value, using scale.

analyseMeanSource

Arguments

:: Sample 
-> Int

Number of iterations used to compute the sample.

-> Criterion Double 

Display the mean of a Sample, and characterise the outliers present in the sample.

countOutliers :: Outliers -> Int64Source

Count the total number of outliers in a sample.

classifyOutliers :: Sample -> OutliersSource

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

noteOutliers :: Outliers -> Criterion ()Source

Display a report of the Outliers present in a Sample.

outlierVarianceSource

Arguments

:: Estimate

Bootstrap estimate of sample mean.

-> Estimate

Bootstrap estimate of sample standard deviation.

-> Double

Number of original iterations.

-> OutlierVariance 

Compute the extent to which outliers in the sample data affect the sample mean and standard deviation.