The probable package

[Tags: bsd3, library]

Easy and reasonably efficient probabilistic programming and random generation

This library gives a common language to speak about probability distributions and random generation, by wrapping both, when necessary, in a RandT monad defined in Math.Probable.Random. This module also provides a lot of useful little combinators for easily describing how random values for your types should be generated.

In Math.Probable.Distribution, you'll find functions for generating random values that follow any distribution supported by mwc-random.

In Math.Probable.Distribution.Finite, you'll find an adaptation of Eric Kidd's work on probability monads (from here).

You may want to check the examples bundled with this package, viewable online at https://github.com/alpmestan/probable/tree/master/examples. One of these examples is simple enough to be worth reproducing here.

 module Main where

 import Control.Applicative
 import Control.Monad
 import Math.Probable

 import qualified Data.Vector.Unboxed as VU

 data Person = Person Int    -- ^ age
                      Double -- ^ weight (kgs)
                      Double -- ^ salary (e.g euros)
     deriving (Eq, Show)

 person :: RandT IO Person
 person =
     Person <$> uniformIn (1, 100)
            <*> uniformIn (2, 130)
            <*> uniformIn (500, 10000)

 randomPersons :: Int -> IO [Person]
 randomPersons n = mwc $ listOf n person

 randomDoubles :: Int -> IO (VU.Vector Double)
 randomDoubles n = mwc $ vectorOf n double

 main :: IO ()
 main = do
     randomPersons 10 >>= mapM_ print
     randomDoubles 10 >>= VU.mapM_ print

Please report any feature request or problem, either by email or through github's issues/feature requests.


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Properties

Versions0.1.0.0, 0.1.1
Change logNone available
Dependenciesbase (>=4.5 && <4.9), mtl, mwc-random (>=0.10), primitive, statistics (>=0.10), transformers (>=0.3), vector (>=0.10) [details]
LicenseBSD3
Copyright2014-2015 Alp Mestanogullari
AuthorAlp Mestanogullari
Maintaineralpmestan@gmail.com
CategoryMath, Statistics
Home pagehttp://github.com/alpmestan/probable
Bug trackerhttp://github.com/alpmestan/probable/issues
Source repositoryhead: git clone https://github.com/alpmestan/probable.git
UploadedTue Jul 28 12:01:24 UTC 2015 by AlpMestanogullari
DistributionsNixOS:0.1.1
Downloads255 total (23 in last 30 days)
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StatusDocs pending
Build status unknown [no reports yet]

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Readme for probable-0.1.1

probable

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Simple random value generation for haskell, using an efficient random generator and minimizing system calls. But the library also lets you work with distributions over a finite set, adapting code from Eric Kidd's posts, and all the usual distributions covered in the statistics package.

You can see how it looks in examples, or below. You can view the documentation for 0.1 here.

Example

Simple example of random generation for your types, using probable.

module Main where

import Control.Applicative
import Control.Monad
import Math.Probable

import qualified Data.Vector.Unboxed as VU

data Person = Person 
    { age    :: Int
    , weight :: Double
    , salary :: Int
    } deriving (Eq, Show)

person :: RandT IO Person
person = 
    Person <$> intIn (1, 100)
           <*> doubleIn (2, 130)
           <*> intIn (500, 10000)

randomPersons :: Int -> IO [Person]
randomPersons n = mwc $ listOf n person

randomDoubles :: Int -> IO (VU.Vector Double)
randomDoubles n = mwc $ vectorOf n double

main :: IO ()
main = do
    randomPersons 10 >>= mapM_ print
    randomDoubles 10 >>= VU.mapM_ print

Distributions over finite sets, conditional probabilities and random sampling.

module Main where

import Math.Probable

import qualified Data.Vector as V

data Book = Interesting 
          | Boring
    deriving (Eq, Show)

bookPrior :: Finite d => d Book
bookPrior = weighted [ (Interesting, 0.2) 
                     , (Boring, 0.8) 
                     ]

twoBooks :: Finite d => d (Book, Book)
twoBooks = do
    book1 <- bookPrior
    book2 <- bookPrior
    return (book1, book2)

sampleBooks :: RandT IO (V.Vector Book)
sampleBooks = vectorOf 10 bookPrior

oneInteresting :: Fin (Book, Book)
oneInteresting = bayes $ do
    (b1, b2) <- twoBooks
    condition (b1 == Interesting || b2 == Interesting)
    return (b1, b2)

main :: IO ()
main = do
    print $ exact bookPrior
    mwc sampleBooks >>= print
    print $ exact twoBooks
    print $ exact oneInteresting

Contact

This library is written and maintained by Alp Mestanogullari.

Feel free to contact me for any feedback, comment, suggestion, bug report and what not.