{-# LANGUAGE BangPatterns #-} {-# LANGUAGE TypeOperators #-} {-# LANGUAGE TypeApplications #-} {-# LANGUAGE ScopedTypeVariables #-} -- | -- Module: : Data.Array.Accelerate.System.Random.MWC -- Copyright : [2014..2020] Trevor L. McDonell -- License : BSD3 -- -- Maintainer : Trevor L. McDonell <trevor.mcdonell@gmail.com> -- Stability : experimental -- Portability : non-portable (GHC extensions) -- -- Random number generation backed by MWC. -- -- [/Example/] -- -- Create a vector of 100 random uniformly distributed floating-point numbers, -- where the PRNG is seeded with data from the system's source of pseudo-random -- numbers (see 'R.withSystemRandom'): -- -- >>> vs <- randomArray uniform (Z :. 100) :: IO (Vector Float) -- -- To generate uniformly distributed random variables in the range (-1,1]: -- -- >>> vs <- randomArray (uniformR (-1,1)) (Z:.100) :: IO (Vector Double) -- -- You can also pass the generator state in explicitly, so that it can be -- reused: -- -- >>> gen <- create :: IO GenIO -- >>> vs <- randomArrayWith gen uniform (Z :. 100) :: IO (Vector Int) -- -- [/Non-uniform distributions/] -- -- If you require random numbers following other distributions, you can combine -- this package with the generators from the -- <http://hackage.haskell.org/package/random-fu random-fu> package. For -- example: -- -- @ -- import Data.Random hiding ( uniform ) -- import qualified Data.Random.Distribution.Exponential as R -- import qualified Data.Random.Distribution.Poisson as R -- -- exponential -- :: (Distribution StdUniform e, Floating e, Shape sh, Elt e) -- => e -- -> sh :~> e -- exponential beta _sh gen = sampleFrom gen (R.exponential beta) -- -- poisson -- :: (Distribution (R.Poisson b) a, Shape sh, Elt a) -- => b -- -> sh :~> a -- poisson lambda _sh gen = sampleFrom gen (R.poisson lambda) -- @ -- -- Which can then be used as before: -- -- >>> vs <- randomArray (exponential 5) (Z :. 100) :: IO (Vector Float) -- >>> us <- randomArray (poisson 5) (Z :. 100) :: IO (Vector Float) -- module Data.Array.Accelerate.System.Random.MWC ( -- * Generating random arrays (:~>), uniform, uniformR, randomArray, randomArrayWith, -- Re-export MWC-Random module System.Random.MWC, ) where import Prelude as P import System.Random.MWC hiding ( uniform, uniformR ) import qualified System.Random.MWC as R import Data.Array.Accelerate.Array.Data import Data.Array.Accelerate.Sugar.Array import Data.Array.Accelerate.Sugar.Elt import Data.Array.Accelerate.Sugar.Shape import qualified Data.Array.Accelerate.Representation.Array as R -- | A PRNG from indices to variates -- type sh :~> e = sh -> GenIO -> IO e -- | Uniformly distributed random variates. -- {-# INLINE uniform #-} uniform :: (Shape sh, Elt e, Variate e) => sh :~> e uniform _ = R.uniform -- | Uniformly distributed random variates in a given range. -- {-# INLINE uniformR #-} uniformR :: (Shape sh, Elt e, Variate e) => (e, e) -> sh :~> e uniformR bounds _ = R.uniformR bounds -- | Generate an array of random values. The generator for variates is -- seeded from the system's fast source of pseudo-random numbers (see: -- 'R.createSystemRandom') -- {-# INLINEABLE randomArray #-} randomArray :: (Shape sh, Elt e) => sh :~> e -> sh -> IO (Array sh e) randomArray f sh = do gen <- createSystemRandom randomArrayWith gen f sh -- | Generate an array of random values using the supplied generator. -- {-# INLINEABLE randomArrayWith #-} randomArrayWith :: (Shape sh, Elt e) => GenIO -> sh :~> e -> sh -> IO (Array sh e) randomArrayWith gen f sh = do adata <- runRandomArray f sh gen return $ adata `seq` Array (R.Array (fromElt sh) adata) -- Create a mutable array and fill it with random values -- {-# INLINEABLE runRandomArray #-} runRandomArray :: forall sh e. (Shape sh, Elt e) => sh :~> e -> sh -> GenIO -> IO (MutableArrayData (EltR e)) runRandomArray f sh gen = do let n = size sh arr <- newArrayData (eltR @e) n -- let write !i | i P.>= n = return () | otherwise = do writeArrayData (eltR @e) arr i . fromElt =<< f (fromIndex sh i) gen write (i+1) -- write 0 return arr