Define sized random generators for almost any type.
{-# LANGUAGE DeriveDataTypeable #-}
import Data.Data
import Test.QuickCheck
import Data.Random.Generics
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.
- Works with QuickCheck and MonadRandom.
- Can be extended or modified with user defined generators.
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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.
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The numerical evaluation of recursively defined generating functions is taken from Boltzmann Oracle for Combinatorial Systems, C. Pivoteau, B. Salvy, M. Soria.