# probable

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
```

This library is written and maintained by Alp Mestanogullari.

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