https://github.com/lysxia/boltzmann-samplers

https://github.com/lysxia/boltzmann-samplers

Last synced: about 1 year ago
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• Host: GitHub
• URL: https://github.com/lysxia/boltzmann-samplers
• Owner: Lysxia
• License: mit
• Created: 2017-03-05T20:11:28.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2018-05-31T15:04:43.000Z (about 8 years ago)
• Last Synced: 2024-04-26T00:25:12.604Z (about 2 years ago)
• Language: Haskell
• Size: 65.4 KB
• Stars: 9
• Watchers: 4
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

          
Boltzmann samplers [![Hackage](https://img.shields.io/hackage/v/boltzmann-samplers.svg)](https://hackage.haskell.org/package/boltzmann-samplers) [![Build Status](https://travis-ci.org/Lysxia/boltzmann-samplers.svg)](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.

See also

--------

- [boltzmann-brain](https://hackage.haskell.org/package/boltzmann-brain-1.3.1.3),

  Boltzmann sampler compiler for combinatorial systems.