Statistics.Sample.WelfordOnlineMeanVariance

Documentation

data WelfordExistingAggregate a

For the storage of required information.

Constructors

WelfordExistingAggregateEmpty

Emtpy aggregate. Needed as a can be of any type, which, hence, allows us to postpone determining a to when we receive the first value.

WelfordExistingAggregate

Fields

• welfordCountUnsafe :: !Int
• welfordMeanUnsafe :: !a
• welfordM2Unsafe :: !a

Instances

Generic (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Associated Types

type Rep (WelfordExistingAggregate a) :: Type -> Type

Methods

from :: WelfordExistingAggregate a -> Rep (WelfordExistingAggregate a) x

to :: Rep (WelfordExistingAggregate a) x -> WelfordExistingAggregate a

Read a => Read (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

readsPrec :: Int -> ReadS (WelfordExistingAggregate a)

readList :: ReadS [WelfordExistingAggregate a]

readPrec :: ReadPrec (WelfordExistingAggregate a)

readListPrec :: ReadPrec [WelfordExistingAggregate a]

Show a => Show (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

showsPrec :: Int -> WelfordExistingAggregate a -> ShowS

show :: WelfordExistingAggregate a -> String

showList :: [WelfordExistingAggregate a] -> ShowS

Serialize a => Serialize (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

put :: Putter (WelfordExistingAggregate a)

get :: Get (WelfordExistingAggregate a)

NFData a => NFData (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

rnf :: WelfordExistingAggregate a -> ()

Eq a => Eq (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

(==) :: WelfordExistingAggregate a -> WelfordExistingAggregate a -> Bool

(/=) :: WelfordExistingAggregate a -> WelfordExistingAggregate a -> Bool

type Rep (WelfordExistingAggregate a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

type Rep (WelfordExistingAggregate a) = D1 ('MetaData "WelfordExistingAggregate" "Statistics.Sample.WelfordOnlineMeanVariance" "welford-online-mean-variance-0.2.0.0-L6Fs4vF6LiXEfWZpb878mM" 'False) (C1 ('MetaCons "WelfordExistingAggregateEmpty" 'PrefixI 'False) (U1 :: Type -> Type) :+: C1 ('MetaCons "WelfordExistingAggregate" 'PrefixI 'True) (S1 ('MetaSel ('Just "welfordCountUnsafe") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Int) :*: (S1 ('MetaSel ('Just "welfordMeanUnsafe") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 a) :*: S1 ('MetaSel ('Just "welfordM2Unsafe") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 a))))

class WelfordOnline a where

Class for all data structures that can be used to computer the Welford approximation. For instance, this can be used to compute the Welford algorithm on a Vectors of Fractional, while only requiring to handle one WelfordExistingAggregate.

Methods

plus infixl 6

Arguments

:: a

-> a

-> a

Addition.

minus infixl 6

Arguments

:: a

-> a

-> a

Subtraction.

multiply infixl 7

Arguments

:: a

-> a

-> a

Multiplication.

divide infixl 7

Arguments

:: a

-> a

-> a

Division.

divideInt infixl 7

Arguments

:: a

-> Int

-> a

Division by Integer.

constInt

Arguments

:: a

-> Int

-> a

Takes one example vector and a integer value and returns a vector of this integer.

squareRootMax

Arguments

:: a

-> a

Compute the square root. Ensure the output is >=1e-3. Used for normalisation.

clipValue

Arguments

:: Double

-> a

-> a

Clip the value(s) to the given range. Used for normalisation.

Instances

WelfordOnline Rational

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Rational -> Rational -> Rational

minus :: Rational -> Rational -> Rational

multiply :: Rational -> Rational -> Rational

divide :: Rational -> Rational -> Rational

divideInt :: Rational -> Int -> Rational

constInt :: Rational -> Int -> Rational

squareRootMax :: Rational -> Rational

clipValue :: Double -> Rational -> Rational

WelfordOnline Double

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Double -> Double -> Double

minus :: Double -> Double -> Double

multiply :: Double -> Double -> Double

divide :: Double -> Double -> Double

divideInt :: Double -> Int -> Double

constInt :: Double -> Int -> Double

squareRootMax :: Double -> Double

clipValue :: Double -> Double -> Double

WelfordOnline Float

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Float -> Float -> Float

minus :: Float -> Float -> Float

multiply :: Float -> Float -> Float

divide :: Float -> Float -> Float

divideInt :: Float -> Int -> Float

constInt :: Float -> Int -> Float

squareRootMax :: Float -> Float

clipValue :: Double -> Float -> Float

WelfordOnline a => WelfordOnline (Vector a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Vector a -> Vector a -> Vector a

minus :: Vector a -> Vector a -> Vector a

multiply :: Vector a -> Vector a -> Vector a

divide :: Vector a -> Vector a -> Vector a

divideInt :: Vector a -> Int -> Vector a

constInt :: Vector a -> Int -> Vector a

squareRootMax :: Vector a -> Vector a

clipValue :: Double -> Vector a -> Vector a

(WelfordOnline a, Storable a) => WelfordOnline (Vector a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Vector a -> Vector a -> Vector a

minus :: Vector a -> Vector a -> Vector a

multiply :: Vector a -> Vector a -> Vector a

divide :: Vector a -> Vector a -> Vector a

divideInt :: Vector a -> Int -> Vector a

constInt :: Vector a -> Int -> Vector a

squareRootMax :: Vector a -> Vector a

clipValue :: Double -> Vector a -> Vector a

(WelfordOnline a, Unbox a) => WelfordOnline (Vector a)

Defined in Statistics.Sample.WelfordOnlineMeanVariance

Methods

plus :: Vector a -> Vector a -> Vector a

minus :: Vector a -> Vector a -> Vector a

multiply :: Vector a -> Vector a -> Vector a

divide :: Vector a -> Vector a -> Vector a

divideInt :: Vector a -> Int -> Vector a

constInt :: Vector a -> Int -> Vector a

squareRootMax :: Vector a -> Vector a

clipValue :: Double -> Vector a -> Vector a

newWelfordAggregateDef :: WelfordOnline a => a -> WelfordExistingAggregate a

Create a new empty Aggreate by specifying an example a value. It is safe to use the `*Unsafe` record field selectors from `WelfordExistingAggregate a`, when creating the data structure using this fuction.

newWelfordAggregate :: WelfordExistingAggregate a

Create a new empty Aggreate for the calculation.

welfordCount :: WelfordExistingAggregate a -> Int

Get counter.

mWelfordMean :: WelfordExistingAggregate a -> Maybe a

Get mean safely, returns Nothing if WelfordExistingAggregateEmpty as the type of a (e.g. length of vector) is unknown.

welfordMeanDef :: WelfordOnline a => a -> WelfordExistingAggregate a -> a

Get mean with specifying a default value.

addValue :: WelfordOnline a => WelfordExistingAggregate a -> a -> WelfordExistingAggregate a

Add one value to the current aggregate.

addValues :: (WelfordOnline a, Foldable f) => WelfordExistingAggregate a -> f a -> WelfordExistingAggregate a

Add multiple values to the current aggregate. This is `foldl addValue`.

mFinalize :: WelfordOnline a => WelfordExistingAggregate a -> Maybe (Mean a, Variance a, SampleVariance a)

Calculate mean, variance and sample variance from aggregate. Safe function.

finalize :: WelfordOnline a => WelfordExistingAggregate a -> (Mean a, Variance a, SampleVariance a)

Calculate mean, variance and sample variance from aggregate. Calls error for WelfordExistingAggregateEmpty.

nextValue :: WelfordOnline a => WelfordExistingAggregate a -> a -> (WelfordExistingAggregate a, (Mean a, Variance a, SampleVariance a))

Add a new sample to the aggregate and compute mean and variances.

isWelfordExistingAggregateEmpty :: WelfordExistingAggregate a -> Bool

Check if it is aggregate is empty

normaliseToZeroMeanUnitVariance :: WelfordOnline a => WelfordExistingAggregate a -> a -> a

Normalise the given input assuming the learned standard deviation with sample variance to zero mean and unit variance. For the first 100 values the output is clipped to (-3, 3).

denormaliseFromZeroMeanUnitVariance :: WelfordOnline a => WelfordExistingAggregate a -> a -> a

Denormalise from a zero mean unit variance normalised value (see normaliseToZeroMeanUnitVariance) to the original value(s).

type Mean a = a

type Variance a = a

type SampleVariance a = a