# patperry/hs-monte-carlo

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 {-# LANGUAGE TypeFamilies #-} ----------------------------------------------------------------------------- -- | -- Module : Control.Monad.MC.Base -- Copyright : Copyright (c) 2010, Patrick Perry -- License : BSD3 -- Maintainer : Patrick Perry -- Stability : experimental -- module Control.Monad.MC.Base where import Control.Monad import qualified Control.Monad.MC.GSLBase as GSL class HasRNG m where -- | The random number generator type for the monad. type RNG m class (Monad m, HasRNG m) => MonadMC m where -- | Get the current random number generator. getRNG :: m (RNG m) -- | Set the current random number generator. setRNG :: RNG m -> m () -- | @uniform a b@ generates a value uniformly distributed in @[a,b)@. uniform :: Double -> Double -> m Double -- | @uniformInt n@ generates an integer uniformly in the range @[0,n-1]@. -- It is an error to call this function with a non-positive value. uniformInt :: Int -> m Int -- | @normal mu sigma@ generates a Normal random variable with mean -- @mu@ and standard deviation @sigma@. normal :: Double -> Double -> m Double -- | @exponential mu@ generates an Exponential variate with mean @mu@. exponential :: Double -> m Double -- | @levy c alpha@ gets a Levy alpha-stable variate with scale @c@ and -- exponent @alpha@. The algorithm only works for @0 < alpha <= 2@. levy :: Double -> Double -> m Double -- | @levySkew c alpha beta @ gets a skew Levy alpha-stable variate -- with scale @c@, exponent @alpha@, and skewness @beta@. The skew -- parameter must lie in the range @[-1,1]@. The algorithm only works -- for @0 < alpha <= 2@. levySkew :: Double -> Double -> Double -> m Double -- | @poisson mu@ generates a Poisson random variable with mean @mu@. poisson :: Double -> m Int -- | Get the baton from the Monte Carlo monad without performing any -- computations. Useful but dangerous. unsafeInterleaveMC :: m a -> m a -- | Generate 'True' events with the given probability bernoulli :: (MonadMC m) => Double -> m Bool bernoulli p = liftM (< p) \$ uniform 0 1 {-# INLINE bernoulli #-} ------------------------------- Instances ----------------------------------- instance HasRNG GSL.MC where type RNG GSL.MC = GSL.RNG instance MonadMC GSL.MC where getRNG = GSL.getRNG {-# INLINE getRNG #-} setRNG = GSL.setRNG {-# INLINE setRNG #-} uniform = GSL.uniform {-# INLINE uniform #-} uniformInt = GSL.uniformInt {-# INLINE uniformInt #-} normal = GSL.normal {-# INLINE normal #-} exponential = GSL.exponential {-# INLINE exponential #-} levy = GSL.levy {-# INLINE levy #-} levySkew = GSL.levySkew {-# INLINE levySkew #-} poisson = GSL.poisson {-# INLINE poisson #-} unsafeInterleaveMC = GSL.unsafeInterleaveMC {-# INLINE unsafeInterleaveMC #-} instance (Monad m) => HasRNG (GSL.MCT m) where type RNG (GSL.MCT m) = GSL.RNG instance (Monad m) => MonadMC (GSL.MCT m) where getRNG = GSL.liftMCT GSL.getRNG {-# INLINE getRNG #-} setRNG r = GSL.liftMCT \$ GSL.setRNG r {-# INLINE setRNG #-} uniform a b = GSL.liftMCT \$ GSL.uniform a b {-# INLINE uniform #-} uniformInt n = GSL.liftMCT \$ GSL.uniformInt n {-# INLINE uniformInt #-} normal mu sigma = GSL.liftMCT \$ GSL.normal mu sigma {-# INLINE normal #-} exponential mu = GSL.liftMCT \$ GSL.exponential mu {-# INLINE exponential #-} levy c alpha = GSL.liftMCT \$ GSL.levy c alpha {-# INLINE levy #-} levySkew c alpha beta = GSL.liftMCT \$ GSL.levySkew c alpha beta {-# INLINE levySkew #-} poisson mu = GSL.liftMCT \$ GSL.poisson mu {-# INLINE poisson #-} unsafeInterleaveMC = GSL.unsafeInterleaveMCT {-# INLINE unsafeInterleaveMC #-}
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