Modulecriterion-1.6.4.0Haskell2010

Criterion.Analysis

Analysis code for benchmarks.

4 types
10 values

Packagecriterion-1.6.4.0
Exports14
LanguageHaskell2010
LicenceBSD-3-Clause
SourceAnalysis.hs

datadata Outliers

Outliers from sample data, calculated using the boxplot technique.

Constructors

Outliers
- 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.

Instances12Eq, Data, Read, Show, Generic, Semigroup, …

Eq OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Data OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Read OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Show OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Generic OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Semigroup OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Monoid OutliersDefined in criterion-1.6.4.0 · Criterion.Types
NFData OutliersDefined in criterion-1.6.4.0 · Criterion.Types
Binary OutliersDefined in criterion-1.6.4.0 · Criterion.Types
FromJSON OutliersDefined in criterion-1.6.4.0 · Criterion.Types
ToJSON OutliersDefined in criterion-1.6.4.0 · Criterion.Types
type Rep Outliers = D1 ('MetaData "Outliers" "Criterion.Types" "criterion-1.6.4.0-3LsyrOZS2k5HHO3ZbYZ39I" 'False) (C1 ('MetaCons "Outliers" 'PrefixI 'True) ((S1 ('MetaSel ('Just "samplesSeen") 'NoSourceUnpackedness 'SourceStrict 'DecidedUnpack) (Rec0 Int64) :*: S1 ('MetaSel ('Just "lowSevere") 'NoSourceUnpackedness 'SourceStrict 'DecidedUnpack) (Rec0 Int64)) :*: (S1 ('MetaSel ('Just "lowMild") 'NoSourceUnpackedness 'SourceStrict 'DecidedUnpack) (Rec0 Int64) :*: (S1 ('MetaSel ('Just "highMild") 'NoSourceUnpackedness 'SourceStrict 'DecidedUnpack) (Rec0 Int64) :*: S1 ('MetaSel ('Just "highSevere") 'NoSourceUnpackedness 'SourceStrict 'DecidedUnpack) (Rec0 Int64)))))Defined in criterion-1.6.4.0 · Criterion.Types

datadata OutlierEffect

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).

Instances11Eq, Data, Ord, Read, Show, Generic, …

Eq OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Data OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Ord OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Read OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Show OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Generic OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
NFData OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
Binary OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
FromJSON OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
ToJSON OutlierEffectDefined in criterion-1.6.4.0 · Criterion.Types
type Rep OutlierEffect = D1 ('MetaData "OutlierEffect" "Criterion.Types" "criterion-1.6.4.0-3LsyrOZS2k5HHO3ZbYZ39I" 'False) ((C1 ('MetaCons "Unaffected" 'PrefixI 'False) U1 :+: C1 ('MetaCons "Slight" 'PrefixI 'False) U1) :+: (C1 ('MetaCons "Moderate" 'PrefixI 'False) U1 :+: C1 ('MetaCons "Severe" 'PrefixI 'False) U1))Defined in criterion-1.6.4.0 · Criterion.Types

datadata OutlierVariance

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

Constructors

OutlierVariance
- 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).

Instances10Eq, Data, Read, Show, Generic, NFData, …

Eq OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
Data OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
Read OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
Show OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
Generic OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
NFData OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
Binary OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
FromJSON OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
ToJSON OutlierVarianceDefined in criterion-1.6.4.0 · Criterion.Types
type Rep OutlierVariance = D1 ('MetaData "OutlierVariance" "Criterion.Types" "criterion-1.6.4.0-3LsyrOZS2k5HHO3ZbYZ39I" 'False) (C1 ('MetaCons "OutlierVariance" 'PrefixI 'True) (S1 ('MetaSel ('Just "ovEffect") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 OutlierEffect) :*: (S1 ('MetaSel ('Just "ovDesc") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 String) :*: S1 ('MetaSel ('Just "ovFraction") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Double))))Defined in criterion-1.6.4.0 · Criterion.Types

datadata SampleAnalysis

Result of a bootstrap analysis of a non-parametric sample.

Constructors

SampleAnalysis
- anRegress :: [Regression]
  Estimates calculated via linear regression.
- anMean :: Estimate ConfInt Double
  Estimated mean.
- anStdDev :: Estimate ConfInt Double
  Estimated standard deviation.
- anOutlierVar :: OutlierVariance
  Description of the effects of outliers on the estimated variance.

Instances9Eq, Read, Show, Generic, NFData, Binary, …

Eq SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
Read SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
Show SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
Generic SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
NFData SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
Binary SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
FromJSON SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
ToJSON SampleAnalysisDefined in criterion-1.6.4.0 · Criterion.Types
type Rep SampleAnalysis = D1 ('MetaData "SampleAnalysis" "Criterion.Types" "criterion-1.6.4.0-3LsyrOZS2k5HHO3ZbYZ39I" 'False) (C1 ('MetaCons "SampleAnalysis" 'PrefixI 'True) ((S1 ('MetaSel ('Just "anRegress") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 [Regression]) :*: S1 ('MetaSel ('Just "anMean") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 (Estimate ConfInt Double))) :*: (S1 ('MetaSel ('Just "anStdDev") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 (Estimate ConfInt Double)) :*: S1 ('MetaSel ('Just "anOutlierVar") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 OutlierVariance))))Defined in criterion-1.6.4.0 · Criterion.Types

valueanalyseSample

:: Int
Experiment number.
-> String
Experiment name.
-> Vector Measured
Sample data.
-> ExceptT String Criterion Report

Perform an analysis of a measurement.

valuescale

:: Double
Value to multiply by.
-> SampleAnalysis
-> SampleAnalysis

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

valueanalyseMean

:: 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.

valuecountOutliers :: Outliers -> Int64

Count the total number of outliers in a sample.

valueclassifyOutliers :: Sample -> Outliers

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

valuenoteOutliers :: Outliers -> Criterion ()

Display a report of the Outliers present in a Sample.

valueoutlierVariance

:: Estimate ConfInt Double
Bootstrap estimate of sample mean.
-> Estimate ConfInt Double
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.

valueresolveAccessors

:: [String]
-> Either String [(String, Measured -> Maybe Double)]

Given a list of accessor names (see measureKeys), return either a mapping from accessor name to function or an error message if any names are wrong.

valuevalidateAccessors

:: [String]
Predictor names.
-> String
Responder name.
-> Either String [(String, Measured -> Maybe Double)]

Given predictor and responder names, do some basic validation, then hand back the relevant accessors.

valueregress

:: GenIO
-> [String]
Predictor names.
-> String
Responder name.
-> Vector Measured
-> ExceptT String Criterion Regression

Regress the given predictors against the responder.

Errors may be returned under various circumstances, such as invalid names or lack of needed data.

See olsRegress for details of the regression performed.