foundation-0.0.5: Alternative prelude with batteries and no dependencies

LicenseBSD-style
Stabilityexperimental
PortabilityGood
Safe HaskellNone
LanguageHaskell2010

Foundation.Random

Description

This module deals with the random subsystem abstractions.

It provide 2 different set of abstractions:

  • The first abstraction that allow a monad to generate random through the MonadRandom class.
  • The second abstraction to make generic random generator RandomGen and a small State monad like wrapper MonadRandomState to abstract a generator.

Synopsis

Documentation

class (Functor m, Applicative m, Monad m) => MonadRandom m where Source #

A monad constraint that allows to generate random bytes

Minimal complete definition

getRandomBytes

newtype MonadRandomState gen a Source #

A simple Monad class very similar to a State Monad with the state being a RandomGenerator.

Constructors

MonadRandomState 

Fields

class RandomGen gen where Source #

A Deterministic Random Generator (DRG) class

Minimal complete definition

randomNew, randomNewFrom, randomGenerate

Methods

randomNew :: MonadRandom m => m gen Source #

Initialize a new random generator

randomNewFrom :: UArray Word8 -> Maybe gen Source #

Initialize a new random generator from a binary seed.

If Nothing is returned, then the data is not acceptable for creating a new random generator.

randomGenerate :: Size Word8 -> gen -> (UArray Word8, gen) Source #

Generate N bytes of randomness from a DRG

getRandomPrimType :: forall randomly ty. (PrimType ty, MonadRandom randomly) => randomly ty Source #

withRandomGenerator :: RandomGen gen => gen -> MonadRandomState gen a -> (a, gen) Source #

Run a pure computation with a Random Generator in the MonadRandomState

type RNG = RNGv1 Source #

An alias to the default choice of deterministic random number generator

Unless, you want to have the stability of a specific random number generator, e.g. for tests purpose, it's recommended to use this alias so that you would keep up to date with possible bugfixes, or change of algorithms.

data RNGv1 Source #

RNG based on ChaCha core.

The algorithm is identical to the arc4random found in recent BSDs, namely a ChaCha core provide 64 bytes of random from 32 bytes of key.