Generic random generators

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 DeriveGeneric #-}

    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 uniform

    -- 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, with compile-time checks.
• 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:

• The core theory of Boltzmann samplers is described in Boltzmann Samplers for the Random Generation of Combinatorial Structures, P. Duchon, P. Flajolet, G. Louchard, G. Schaeffer.

• The numerical evaluation of recursively defined generating functions is taken from Boltzmann Oracle for Combinatorial Systems, C. Pivoteau, B. Salvy, M. Soria.