Safe Haskell	None
Language	Haskell2010

System.Random.Reservoir

Contents

Unweighted Sampling

Description

Module : System.Random.Reservoir
Description : Reservoir Samplers
Copyright : (c) Mark Hay, 2018
License : BSD3
Maintainer : mah6@williams.edu
Stability : experimental
Reservoir algorithms suitable for sampling from data streams of large or unknown size.

Synopsis

Unweighted Sampling

data Res g a Source #

Wrapper for the state kept by algorithm R. Keeps the sample as an IntMap, the number of elements seen, and the random seed.

Constructors

Res
Fields resSample :: !(IntMap a) The current sample. Should usually not be modified by hand. numSeen :: !Int The number of elements seen. Should almost never be modified by hand. resSeed :: !g The random seed for the sample

Instances

Functor (Res g) Source #
Methods fmap :: (a -> b) -> Res g a -> Res g b # (<$) :: a -> Res g b -> Res g a #
(Show g, Show a) => Show (Res g a) Source #
Methods showsPrec :: Int -> Res g a -> ShowS # show :: Res g a -> String # showList :: [Res g a] -> ShowS #

getSample :: Res g a -> [a] Source #

Extracts the sample as a list.

emptyRes :: RandomGen g => g -> Res g a Source #

Creates a Res with nothing in the sample and with the counter at zero.

emptyRes' :: Word64 -> Res PureMT a Source #

A version of emptyRes specialized to System.Random.Mersenne.Pure64.

reservoirSample Source #

Arguments

:: RandomGen g
=> Int	The maximum number of elements to be in the sample.
-> a	The next element to be considered
-> Res g a	The current wrapped sample
-> Res g a

Jeffrey Vitter's "Algorithm R". Given the elements in the existing sample and a new element, every element has a equal probability of being selected. Intended to be partially applied on the sample size and used with fold-like functions to sample large streams of data.