Safe Haskell | Safe-Inferred |
---|---|
Language | Haskell2010 |
Synopsis
- type RVar = RVarT Identity
- runRVar :: StatefulGen g m => RVar a -> g -> m a
- data RVarT (m :: Type -> Type) a
- runRVarT :: (Lift n m, StatefulGen g m) => RVarT n a -> g -> m a
- runRVarTWith :: forall m n g a. StatefulGen g m => (forall t. n t -> m t) -> RVarT n a -> g -> m a
- data RGen = RGen
- uniformRVarT :: forall a (m :: Type -> Type). Uniform a => RVarT m a
- uniformRangeRVarT :: forall a (m :: Type -> Type). UniformRange a => (a, a) -> RVarT m a
Documentation
An opaque type modeling a "random variable" - a value
which depends on the outcome of some random event. RVar
s
can be conveniently defined by an imperative-looking style:
normalPair = do u <- stdUniform t <- stdUniform let r = sqrt (-2 * log u) theta = (2 * pi) * t x = r * cos theta y = r * sin theta return (x,y)
OR by a more applicative style:
logNormal = exp <$> stdNormal
Once defined (in any style), there are several ways to sample RVar
s:
- Using an immutable pseudo-random number generator that has an instance for
RandomGen
withStateT
monad:
>>>
import qualified Data.Random as Fu (uniform)
>>>
import System.Random (mkStdGen)
>>>
import Control.Monad.State (runState)
>>>
runState (sampleStateRVar (Fu.uniform 1 (100 :: Integer))) (mkStdGen 2021)
(79,StdGen {unStdGen = SMGen 4687568268719557181 4805600293067301895})
- Using a mutable pseud-random number generator that has an instance for
StatefulGen
withReaderT
monad.
>>>
import qualified Data.Random as Fu (uniform)
>>>
import System.Random.MWC (create)
>>>
import Control.Monad.Reader (runReaderT)
>>>
import qualified Data.Vector.Storable as VS
>>>
initialize (VS.singleton 2021) >>= runReaderT (sampleReaderRVar (uniform 1 (100 :: Integer)))
8
runRVar :: StatefulGen g m => RVar a -> g -> m a #
"Run" an RVar
- samples the random variable from the provided
source of entropy.
data RVarT (m :: Type -> Type) a #
A random variable with access to operations in an underlying monad. Useful examples include any form of state for implementing random processes with hysteresis, or writer monads for implementing tracing of complicated algorithms.
For example, a simple random walk can be implemented as an RVarT
IO
value:
rwalkIO :: IO (RVarT IO Double) rwalkIO d = do lastVal <- newIORef 0 let x = do prev <- lift (readIORef lastVal) change <- rvarT StdNormal let new = prev + change lift (writeIORef lastVal new) return new return x
To run the random walk it must first be initialized, after which it can be sampled as usual:
do rw <- rwalkIO x <- sampleRVarT rw y <- sampleRVarT rw ...
The same random-walk process as above can be implemented using MTL types
as follows (using import Control.Monad.Trans as MTL
):
rwalkState :: RVarT (State Double) Double rwalkState = do prev <- MTL.lift get change <- rvarT StdNormal let new = prev + change MTL.lift (put new) return new
Invocation is straightforward (although a bit noisy) if you're used to MTL:
rwalk :: Int -> Double -> StdGen -> ([Double], StdGen) rwalk count start gen = flip evalState start . flip runStateT gen . sampleRVarTWith MTL.lift $ replicateM count rwalkState
Instances
MonadTrans RVarT | |
MonadPrompt Prim (RVarT n) | |
StatefulGen RGen (RVarT m) | |
Defined in Data.RVar uniformWord32R :: Word32 -> RGen -> RVarT m Word32 # uniformWord64R :: Word64 -> RGen -> RVarT m Word64 # uniformWord8 :: RGen -> RVarT m Word8 # uniformWord16 :: RGen -> RVarT m Word16 # uniformWord32 :: RGen -> RVarT m Word32 # uniformWord64 :: RGen -> RVarT m Word64 # uniformShortByteString :: Int -> RGen -> RVarT m ShortByteString # | |
MonadIO m => MonadIO (RVarT m) | |
Applicative (RVarT n) | |
Functor (RVarT n) | |
Monad (RVarT n) | |
Lift m n => Sampleable (RVarT m) n t Source # | |
Defined in Data.Random.Sample sampleFrom :: StatefulGen g n => g -> RVarT m t -> n t Source # | |
Lift (RVarT Identity) (RVarT m) Source # | |
runRVarT :: (Lift n m, StatefulGen g m) => RVarT n a -> g -> m a Source #
Like runRVarTWith
, but using an implicit lifting (provided by the
Lift
class)
runRVarTWith :: forall m n g a. StatefulGen g m => (forall t. n t -> m t) -> RVarT n a -> g -> m a #
"Runs" an RVarT
, sampling the random variable it defines.
The first argument lifts the base monad into the sampling monad. This operation must obey the "monad transformer" laws:
lift . return = return lift (x >>= f) = (lift x) >>= (lift . f)
One example of a useful non-standard lifting would be one that takes
State s
to another monad with a different state representation (such as
IO
with the state mapped to an IORef
):
embedState :: (Monad m) => m s -> (s -> m ()) -> State s a -> m a embedState get put = \m -> do s <- get (res,s) <- return (runState m s) put s return res
The ability to lift is very important - without it, every RVar
would have
to either be given access to the full capability of the monad in which it
will eventually be sampled (which, incidentally, would also have to be
monomorphic so you couldn't sample one RVar
in more than one monad)
or functions manipulating RVar
s would have to use higher-ranked
types to enforce the same kind of isolation and polymorphism.
Instances
StatefulGen RGen (RVarT m) | |
Defined in Data.RVar uniformWord32R :: Word32 -> RGen -> RVarT m Word32 # uniformWord64R :: Word64 -> RGen -> RVarT m Word64 # uniformWord8 :: RGen -> RVarT m Word8 # uniformWord16 :: RGen -> RVarT m Word16 # uniformWord32 :: RGen -> RVarT m Word32 # uniformWord64 :: RGen -> RVarT m Word64 # uniformShortByteString :: Int -> RGen -> RVarT m ShortByteString # |
uniformRangeRVarT :: forall a (m :: Type -> Type). UniformRange a => (a, a) -> RVarT m a #