Boltzmann samplers [](https://hackage.haskell.org/package/generic-random) [](https://travis-ci.org/Lysxia/boltzmann-samplers)
==================
`Boltzmann.Data`
----------------
Define sized random generators for `Data.Data` generic types.
```haskell
{-# LANGUAGE DeriveDataTypeable #-}
import Data.Data
import Test.QuickCheck
import Boltzmann.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.
- Works with QuickCheck and MonadRandom, but also similar user-defined monads
for randomness (just implement `MonadRandomLike`).
- Can be tweaked somewhat with user defined generators.
`Boltzmann.Species`
-------------------
An experimental interface to obtain Boltzmann samplers from an applicative
specification of a combinatorial system.
No documentation (yet).
References
----------
- The core theory of Boltzmann samplers is described in
[Boltzmann Samplers for the Random Generation of Combinatorial Structures](http://algo.inria.fr/flajolet/Publications/DuFlLoSc04.pdf),
P. Duchon, P. Flajolet, G. Louchard, G. Schaeffer.
- The numerical evaluation of recursively defined generating functions
is taken from
[Boltzmann Oracle for Combinatorial Systems](http://www.dmtcs.org/pdfpapers/dmAI0132.pdf),
C. Pivoteau, B. Salvy, M. Soria.