generic-random: Generic random generators

[ generics, library, mit, testing ] [ Propose Tags ]

Please see the README.


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Versions 0.1.0.0, 0.1.1.0, 0.2.0.0, 0.3.0.0, 0.4.0.0, 0.4.1.0, 0.5.0.0, 1.0.0.0, 1.1.0.0, 1.1.0.1, 1.1.0.2, 1.2.0.0
Dependencies ad, base (>=4.9 && <5), containers, hashable, hmatrix, ieee754, MonadRandom, mtl, QuickCheck, transformers, unordered-containers, vector [details]
License MIT
Author Li-yao Xia
Maintainer lysxia@gmail.com
Revised Revision 1 made by lyxia at Sun Aug 14 22:31:52 UTC 2016
Category Generics, Testing
Home page http://github.com/lysxia/generic-random
Source repo head: git clone https://github.com/lysxia/generic-random
Uploaded by lyxia at Sun Aug 14 22:27:11 UTC 2016
Distributions LTSHaskell:1.2.0.0, NixOS:1.2.0.0, Stackage:1.2.0.0
Downloads 3626 total (66 in the last 30 days)
Rating 2.25 (votes: 2) [estimated by rule of succession]
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Status Docs available [build log]
Last success reported on 2016-08-14 [all 1 reports]
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Readme for generic-random-0.2.0.0

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Generic random generators Hackage Build Status

Generic.Random.Data

Define sized random generators for almost any type.

    {-# LANGUAGE DeriveDataTypeable #-}

    import Data.Data
    import Test.QuickCheck
    import Generic.Random.Data

    data Term = Lambda Int Term | App Term Term | Var Int
      deriving (Show, Data)

    instance Arbitrary Term where
      arbitrary = sized $ generatorPWith [positiveInts]

    positiveInts :: Alias Gen
    positiveInts =
      alias $ \() -> fmap getPositive arbitrary :: Gen Int

    main = sample (arbitrary :: Gen Term)
  • Objects of the same size (number of constructors) occur with the same probability (see Duchon et al., references below).
  • Implements rejection sampling and pointing.
  • Uses Data.Data generics.
  • Works with QuickCheck and MonadRandom, but also similar user-defined monads for randomness (just implement MonadRandomLike).
  • Can be tweaked somewhat with user defined generators.

Generic.Random.Generic

Say goodbye to Constructor <$> arbitrary <*> arbitrary <*> arbitrary-boilerplate.

    {-# LANGUAGE DataKinds #-}
    {-# LANGUAGE DeriveGeneric #-}
    {-# LANGUAGE TypeApplications #-}

    import GHC.Generics ( Generic )
    import Test.QuickCheck
    import Generic.Random.Generic

    data Tree a = Leaf | Node (Tree a) a (Tree a)
      deriving (Show, Generic)

    instance Arbitrary a => Arbitrary (Tree a) where
      arbitrary = genericArbitrary' @'Z

    -- Equivalent to
    -- > arbitrary =
    -- >   sized $ \n ->
    -- >     if n == 0 then
    -- >       return Leaf
    -- >     else
    -- >       oneof
    -- >         [ return Leaf
    -- >         , Node <$> arbitrary <*> arbitrary <*> arbitrary
    -- >         ]

    main = sample (arbitrary :: Gen (Tree ()))
  • User-specified distribution of constructors.
  • A simple (optional) strategy to ensure termination: Test.QuickCheck.Gen's size parameter decreases at every recursive genericArbitrary' call; when it reaches zero, sample directly from a finite set of finite values.
  • Uses GHC.Generics generics.
  • Just for QuickCheck's arbitrary.
  • More flexible than Generic.Random.Data's Boltzmann samplers, which compute fixed weights for a given target size and concrete type, but with a less regular distribution.

Generic.Random.Boltzmann

An experimental interface to obtain Boltzmann samplers from an applicative specification of a combinatorial system.

No documentation (yet).

References

Papers about Boltzmann samplers, used in Generic.Random.Data: