criterion-1.5.6.2: Robust, reliable performance measurement and analysis

Copyright(c) 2009-2014 Bryan O'Sullivan
LicenseBSD-style
Maintainerbos@serpentine.com
Stabilityexperimental
PortabilityGHC
Safe HaskellTrustworthy
LanguageHaskell2010

Criterion.Analysis

Description

Analysis code for benchmarks.

Synopsis

Documentation

data Outliers Source #

Outliers from sample data, calculated using the boxplot technique.

Constructors

Outliers 

Fields

Instances
Eq Outliers Source # 
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Defined in Criterion.Types

Data Outliers Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Outliers -> c Outliers #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Outliers #

toConstr :: Outliers -> Constr #

dataTypeOf :: Outliers -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c Outliers) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Outliers) #

gmapT :: (forall b. Data b => b -> b) -> Outliers -> Outliers #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Outliers -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Outliers -> r #

gmapQ :: (forall d. Data d => d -> u) -> Outliers -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Outliers -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Outliers -> m Outliers #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Outliers -> m Outliers #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Outliers -> m Outliers #

Read Outliers Source # 
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Show Outliers Source # 
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Generic Outliers Source # 
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Associated Types

type Rep Outliers :: Type -> Type #

Methods

from :: Outliers -> Rep Outliers x #

to :: Rep Outliers x -> Outliers #

Semigroup Outliers Source # 
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Monoid Outliers Source # 
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NFData Outliers Source # 
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Methods

rnf :: Outliers -> () #

ToJSON Outliers Source # 
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FromJSON Outliers Source # 
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Binary Outliers Source # 
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Methods

put :: Outliers -> Put #

get :: Get Outliers #

putList :: [Outliers] -> Put #

type Rep Outliers Source # 
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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).

Instances
Eq OutlierEffect Source # 
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Defined in Criterion.Types

Data OutlierEffect Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> OutlierEffect -> c OutlierEffect #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c OutlierEffect #

toConstr :: OutlierEffect -> Constr #

dataTypeOf :: OutlierEffect -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c OutlierEffect) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c OutlierEffect) #

gmapT :: (forall b. Data b => b -> b) -> OutlierEffect -> OutlierEffect #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> OutlierEffect -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> OutlierEffect -> r #

gmapQ :: (forall d. Data d => d -> u) -> OutlierEffect -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> OutlierEffect -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> OutlierEffect -> m OutlierEffect #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> OutlierEffect -> m OutlierEffect #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> OutlierEffect -> m OutlierEffect #

Ord OutlierEffect Source # 
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Read OutlierEffect Source # 
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Show OutlierEffect Source # 
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Generic OutlierEffect Source # 
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type Rep OutlierEffect :: Type -> Type #

NFData OutlierEffect Source # 
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Methods

rnf :: OutlierEffect -> () #

ToJSON OutlierEffect Source # 
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FromJSON OutlierEffect Source # 
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Binary OutlierEffect Source # 
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type Rep OutlierEffect Source # 
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type Rep OutlierEffect = D1 (MetaData "OutlierEffect" "Criterion.Types" "criterion-1.5.6.2-FgzuYTP90tcJklLNTLLMjj" False) ((C1 (MetaCons "Unaffected" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "Slight" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "Moderate" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "Severe" PrefixI False) (U1 :: Type -> Type)))

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

Instances
Eq OutlierVariance Source # 
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Defined in Criterion.Types

Data OutlierVariance Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> OutlierVariance -> c OutlierVariance #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c OutlierVariance #

toConstr :: OutlierVariance -> Constr #

dataTypeOf :: OutlierVariance -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c OutlierVariance) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c OutlierVariance) #

gmapT :: (forall b. Data b => b -> b) -> OutlierVariance -> OutlierVariance #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> OutlierVariance -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> OutlierVariance -> r #

gmapQ :: (forall d. Data d => d -> u) -> OutlierVariance -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> OutlierVariance -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> OutlierVariance -> m OutlierVariance #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> OutlierVariance -> m OutlierVariance #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> OutlierVariance -> m OutlierVariance #

Read OutlierVariance Source # 
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Show OutlierVariance Source # 
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Generic OutlierVariance Source # 
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type Rep OutlierVariance :: Type -> Type #

NFData OutlierVariance Source # 
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Methods

rnf :: OutlierVariance -> () #

ToJSON OutlierVariance Source # 
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FromJSON OutlierVariance Source # 
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Binary OutlierVariance Source # 
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type Rep OutlierVariance Source # 
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type Rep OutlierVariance = D1 (MetaData "OutlierVariance" "Criterion.Types" "criterion-1.5.6.2-FgzuYTP90tcJklLNTLLMjj" 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))))

data SampleAnalysis Source #

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

Constructors

SampleAnalysis 

Fields

Instances
Eq SampleAnalysis Source # 
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Read SampleAnalysis Source # 
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Show SampleAnalysis Source # 
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Generic SampleAnalysis Source # 
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type Rep SampleAnalysis :: Type -> Type #

NFData SampleAnalysis Source # 
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Methods

rnf :: SampleAnalysis -> () #

ToJSON SampleAnalysis Source # 
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FromJSON SampleAnalysis Source # 
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Binary SampleAnalysis Source # 
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type Rep SampleAnalysis Source # 
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analyseSample Source #

Arguments

:: Int

Experiment number.

-> String

Experiment name.

-> Vector Measured

Sample data.

-> ExceptT String Criterion Report 

Perform an analysis of a measurement.

scale Source #

Arguments

:: Double

Value to multiply by.

-> SampleAnalysis 
-> SampleAnalysis 

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

analyseMean Source #

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 -> 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 -> Criterion () Source #

Display a report of the Outliers present in a Sample.

outlierVariance Source #

Arguments

:: 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.

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

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.

validateAccessors Source #

Arguments

:: [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.

regress Source #

Arguments

:: 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.