mwc-random 0.13.1.2 → 0.13.2.0

raw patch · 5 files changed

+202/−9 lines, 5 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• ChangeLog +20/−0
• System/Random/MWC/Distributions.hs +110/−8
• benchmarks/tsts.hs +65/−0
• mwc-random.cabal +1/−1
• test/KS.hs +6/−0

@@ -1,7 +1,26 @@+Changes in 0.13.2.0++  * Generators for beta, Bernoully, Dirichlet and categorical distributions+    added.++  * Functions for generating random shuffles added.+++Changes in 0.13.1.2++  * GHC 7.9 support+++Changes in 0.13.1.1++  * Long standing performance problem in normal distribution fixed (#16)++ Changes in 0.13.1.0    * createSystemRandom added + Changes in 0.13.0.0    * Workaround for GHC bug 8072 (bug 25). GHC 7.6 on 32-bit platrofms is@@ -9,6 +28,7 @@    * Generators for truncated exponential and geometric distributions     added.+  Changes in 0.12.0.0 

@@ -1,4 +1,4 @@-{-# LANGUAGE BangPatterns, GADTs #-}+{-# LANGUAGE BangPatterns, GADTs, FlexibleContexts, ScopedTypeVariables #-} -- | -- Module    : System.Random.MWC.Distributions -- Copyright : (c) 2012 Bryan O'Sullivan@@ -13,25 +13,40 @@ module System.Random.MWC.Distributions     (     -- * Variates: non-uniformly distributed values+    -- ** Continuous distributions       normal     , standard     , exponential     , truncatedExp     , gamma     , chiSquare+    , beta+      -- ** Discrete distribution+    , categorical     , geometric0     , geometric1+    , bernoulli+      -- ** Multivariate+    , dirichlet+      -- * Permutations+    , uniformPermutation+    , uniformShuffle      -- * References     -- $references     ) where -import Control.Monad (liftM)+import Prelude hiding (mapM)+import Control.Monad (liftM,when) import Control.Monad.Primitive (PrimMonad, PrimState) import Data.Bits ((.&.))+import Data.Foldable (Foldable,foldl')+import Data.Traversable (Traversable,mapM) import Data.Word (Word32)-import System.Random.MWC (Gen, uniform)-import qualified Data.Vector.Unboxed as I+import System.Random.MWC (Gen, uniform, uniformR)+import qualified Data.Vector.Unboxed         as I+import qualified Data.Vector.Generic         as G+import qualified Data.Vector.Generic.Mutable as M  -- Unboxed 2-tuple data T = T {-# UNPACK #-} !Double {-# UNPACK #-} !Double@@ -117,9 +132,9 @@             -> Gen (PrimState m) -- ^ Generator             -> m Double {-# INLINE exponential #-}-exponential beta gen = do+exponential b gen = do   x <- uniform gen-  return $! - log x / beta+  return $! - log x / b   -- | Generate truncated exponentially distributed random variate.@@ -130,12 +145,12 @@              -> Gen (PrimState m) -- ^ Generator.              -> m Double {-# INLINE truncatedExp #-}-truncatedExp beta (a,b) gen = do+truncatedExp scale (a,b) gen = do   -- We shift a to 0 and then generate distribution truncated to [0,b-a]   -- It's easier   let delta = b - a   p <- uniform gen-  return $! a - log ( (1 - p) + p*exp(-beta*delta)) / beta+  return $! a - log ( (1 - p) + p*exp(-scale*delta)) / scale  -- | Random variate generator for gamma distribution. gamma :: PrimMonad m@@ -208,7 +223,92 @@ geometric1 p gen = do n <- geometric0 p gen                       return $! n + 1 +-- | Random variate generator for Beta distribution+beta :: PrimMonad m+     => Double            -- ^ alpha (>0)+     -> Double            -- ^ beta  (>0)+     -> Gen (PrimState m) -- ^ Generator+     -> m Double+{-# INLINE beta #-}+beta a b gen = do+  x <- gamma a 1 gen+  y <- gamma b 1 gen+  return $! x / (x+y) +-- | Random variate generator for Dirichlet distribution+dirichlet :: (PrimMonad m, Traversable t)+          => t Double          -- ^ container of parameters+          -> Gen (PrimState m) -- ^ Generator+          -> m (t Double)+{-# INLINE dirichlet #-}+dirichlet t gen = do+  t' <- mapM (\x -> gamma x 1 gen) t+  let total = foldl' (+) 0 t'+  return $ fmap (/total) t'++-- | Random variate generator for Bernoulli distribution+bernoulli :: PrimMonad m+          => Double            -- ^ Probability of success (returning True)+          -> Gen (PrimState m) -- ^ Generator+          -> m Bool+{-# INLINE bernoulli #-}+bernoulli p gen = (<p) `liftM` uniform gen++-- | Random variate generator for categorical distribution.+--+--   Note that if you need to generate a lot of variates functions+--   "System.Random.MWC.CondensedTable" will offer better+--   performance.  If only few is needed this function will faster+--   since it avoids costs of setting up table.+categorical :: (PrimMonad m, G.Vector v Double)+            => v Double          -- ^ List of weights [>0]+            -> Gen (PrimState m) -- ^ Generator+            -> m Int+{-# INLINE categorical #-}+categorical v gen+    | G.null v = pkgError "categorical" "empty weights!"+    | otherwise = do+        let cv  = G.scanl1' (+) v+        p <- (G.last cv *) `liftM` uniform gen+        return $! case G.findIndex (>=p) cv of+                    Just i  -> i+                    Nothing -> pkgError "categorical" "bad weights!"++-- | Random variate generator for uniformly distributed permutations.+--   It returns random permutation of vector /[0 .. n-1]/.+--+--   This is the Fisher-Yates shuffle+uniformPermutation :: forall m v. (PrimMonad m, G.Vector v Int)+                   => Int+                   -> Gen (PrimState m)+                   -> m (v Int)+{-# INLINE uniformPermutation #-}+uniformPermutation n gen = do+  when (n<=0) (pkgError "uniformPermutation" "size must be >0")+  v <- G.unsafeThaw (G.generate n id :: v Int)+  let lst = n-1+      loop i | i == lst  = G.unsafeFreeze v+             | otherwise = do+                 j <- uniformR (i,lst) gen+                 M.unsafeSwap v i j+                 loop (i+1)+  loop 0+++-- | Random variate generator for a uniformly distributed shuffle of a+--   vector.+uniformShuffle :: (PrimMonad m, G.Vector v a, G.Vector v Int)+               => v a+               -> Gen (PrimState m)+               -> m (v a)+{-# INLINE uniformShuffle #-}+uniformShuffle xs gen+    | G.length xs <= 1 = return xs+    | otherwise        = do+        idx <- uniformPermutation (G.length xs) gen+        return $! G.backpermute xs idx++ sqr :: Double -> Double sqr x = x * x {-# INLINE sqr #-}@@ -216,6 +316,8 @@ pkgError :: String -> String -> a pkgError func msg = error $ "System.Random.MWC.Distributions." ++ func ++                             ": " ++ msg++  -- $references --

@@ -0,0 +1,65 @@+{-# LANGUAGE BangPatterns #-}+import Control.Monad+import System.Random.MWC+import System.Random.MWC.Distributions++main = do+  withSystemRandom $ \g -> replicateM_ (300*1000) $ do+    --+    !n <- normal 0 1  g+    !n <- normal 0 2  g+    !n <- normal 3 3  g+    !n <- normal 2 4  g+    !n <- normal 2 5  g+    !n <- normal 1 6  g+    !n <- normal 3 7  g+    !n <- normal 3 8  g+    !n <- normal 3 9  g+    !n <- normal 3 10 g+    --+    !n <- normal 0 1  g+    !n <- normal 0 2  g+    !n <- normal 3 3  g+    !n <- normal 2 4  g+    !n <- normal 2 5  g+    !n <- normal 1 6  g+    !n <- normal 3 7  g+    !n <- normal 3 8  g+    !n <- normal 3 9  g+    !n <- normal 3 10 g+    --+    !n <- normal 0 1  g+    !n <- normal 0 2  g+    !n <- normal 3 3  g+    !n <- normal 2 4  g+    !n <- normal 2 5  g+    !n <- normal 1 6  g+    !n <- normal 3 7  g+    !n <- normal 3 8  g+    !n <- normal 3 9  g+    !n <- normal 3 10 g+    --+    !n <- normal 0 1  g+    !n <- normal 0 2  g+    !n <- normal 3 3  g+    !n <- normal 2 4  g+    !n <- normal 2 5  g+    !n <- normal 1 6  g+    !n <- normal 3 7  g+    !n <- normal 3 8  g+    !n <- normal 3 9  g+    !n <- normal 3 10 g+    --+    !n <- normal 0 1  g+    !n <- normal 0 2  g+    !n <- normal 3 3  g+    !n <- normal 2 4  g+    !n <- normal 2 5  g+    !n <- normal 1 6  g+    !n <- normal 3 7  g+    !n <- normal 3 8  g+    !n <- normal 3 9  g+    !n <- normal 3 10 g+    --+    return () :: IO ()+    

@@ -1,5 +1,5 @@ name:           mwc-random-version:        0.13.1.2+version:        0.13.2.0 synopsis:       Fast, high quality pseudo random number generation description:   This package contains code for generating high quality random

@@ -10,10 +10,12 @@ import Statistics.Test.KolmogorovSmirnov  import Statistics.Distribution+import Statistics.Distribution.Binomial import Statistics.Distribution.Exponential import Statistics.Distribution.Gamma import Statistics.Distribution.Normal import Statistics.Distribution.Uniform+import Statistics.Distribution.Beta  import qualified System.Random.MWC               as MWC import qualified System.Random.MWC.Distributions as MWC@@ -38,6 +40,10 @@     -- Exponential   , testCase "exponential l=1"    $ testKS (exponential 1)       (MWC.exponential 1) g   , testCase "exponential l=3"    $ testKS (exponential 3)       (MWC.exponential 3) g+    -- Beta+  , testCase "beta a=0.3,b=0.5"    $ testKS (betaDistr 0.3 0.5)       (MWC.beta 0.3 0.5) g+  , testCase "beta a=0.1,b=0.8"    $ testKS (betaDistr 0.3 0.5)       (MWC.beta 0.3 0.5) g+  , testCase "beta a=0.8,b=0.1"    $ testKS (betaDistr 0.3 0.5)       (MWC.beta 0.3 0.5) g   ]  testKS :: (Distribution d) => d -> (MWC.GenIO -> IO Double) -> MWC.GenIO -> IO ()