{-# LANGUAGE MultiParamTypeClasses, FlexibleInstances, FlexibleContexts, IncoherentInstances #-} module Data.Random.Sample where import Control.Monad.State import Data.Random.Distribution import Data.Random.Lift import Data.Random.RVar import Data.Random.Source import Data.Random.Source.Std -- |A typeclass allowing 'Distribution's and 'RVar's to be sampled. Both may -- also be sampled via 'runRVar' or 'runRVarT', but I find it psychologically -- pleasing to be able to sample both using this function, as they are two -- separate abstractions for one base concept: a random variable. class Sampleable d m t where -- |Directly sample from a distribution or random variable, using the given source of entropy. sampleFrom :: RandomSource m s => s -> d t -> m t instance Distribution d t => Sampleable d m t where sampleFrom src d = runRVarT (rvar d) src -- This instance overlaps with the other, but because RVarT is not a Distribution there is no conflict. instance Lift m n => Sampleable (RVarT m) n t where sampleFrom src x = runRVarT x src -- |Sample a random variable using the default source of entropy for the -- monad in which the sampling occurs. sample :: (Sampleable d m t, MonadRandom m) => d t -> m t sample = sampleFrom StdRandom -- |Sample a random variable in a \"functional\" style. Typical instantiations -- of @s@ are @System.Random.StdGen@ or @System.Random.Mersenne.Pure64.PureMT@. sampleState :: (Sampleable d (State s) t, MonadRandom (State s)) => d t -> s -> (t, s) sampleState thing = runState (sample thing) -- |Sample a random variable in a \"semi-functional\" style. Typical instantiations -- of @s@ are @System.Random.StdGen@ or @System.Random.Mersenne.Pure64.PureMT@. sampleStateT :: (Sampleable d (StateT s m) t, MonadRandom (StateT s m)) => d t -> s -> m (t, s) sampleStateT thing = runStateT (sample thing)