mwc-random 0.12.0.1 → 0.13.0.0

raw patch · 4 files changed

+78/−14 lines, 4 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• System/Random/MWC.hs +25/−6
• System/Random/MWC/CondensedTable.hs +4/−2
• System/Random/MWC/Distributions.hs +48/−5
• mwc-random.cabal +1/−1

@@ -507,14 +507,32 @@ type instance Unsigned Int16 = Word16 type instance Unsigned Int32 = Word32 type instance Unsigned Int64 = Word64-type instance Unsigned Int   = Word  type instance Unsigned Word8  = Word8 type instance Unsigned Word16 = Word16 type instance Unsigned Word32 = Word32 type instance Unsigned Word64 = Word64-type instance Unsigned Word   = Word +-- This is workaround for bug #25.+--+-- GHC-7.6 has a bug (#8072) which results in calculation of wrong+-- number of buckets in function `uniformRange'. Consequently uniformR+-- generates values in wrong range.+--+-- Bug only affects 32-bit systems and Int/Word data types. Word32+-- works just fine. So we set Word32 as unsigned counterpart for Int+-- and Word on 32-bit systems. It's done only for GHC-7.6 because+-- other versions are unaffected by the bug and we expect that GHC may+-- optimise code which uses Word better.+#if (WORD_SIZE_IN_BITS < 64) && (__GLASGOW_HASKELL__ == 706)+type instance Unsigned Int   = Word32+type instance Unsigned Word  = Word32+#else+type instance Unsigned Int   = Word+type instance Unsigned Word  = Word+#endif++ -- Subtract two numbers under assumption that x>=y and store result in -- unsigned data type of same size sub :: (Integral a, Integral (Unsigned a)) => a -> a -> Unsigned a@@ -613,10 +631,11 @@ --   /Communications of the ACM/ 46(5):90&#8211;93. --   <http://doi.acm.org/10.1145/769800.769827> ----- * Thomas, D.B.; Leong, P.G.W.; Luk, W.; Villasenor, J.D.---   (2007). Gaussian random number generators.---   /ACM Computing Surveys/ 39(4).---   <http://www.cse.cuhk.edu.hk/~phwl/mt/public/archives/papers/grng_acmcs07.pdf>+-- * Doornik, J.A. (2007) Conversion of high-period random numbers to+--   floating point.+--   /ACM Transactions on Modeling and Computer Simulation/ 17(1).+--   <http://www.doornik.com/research/randomdouble.pdf>+  -- $typehelp --

@@ -24,6 +24,8 @@     -- ** Disrete distributions   , tablePoisson   , tableBinomial+    -- * References+    -- $references   ) where  import Control.Arrow           (second,(***))@@ -102,7 +104,7 @@ -- the case, this algorithm will construct a table for some -- distribution that may bear no resemblance to what you intended. tableFromProbabilities-    :: (Vector v (a,Word32), Vector v (a,Double), Vector v a, Vector v Word32, Show a)+    :: (Vector v (a,Word32), Vector v (a,Double), Vector v a, Vector v Word32)        => v (a, Double) -> CondensedTable v a {-# INLINE tableFromProbabilities #-} tableFromProbabilities v@@ -124,7 +126,7 @@ -- probilities. Non-positive weights are discarded, and those -- remaining are normalized to 1. tableFromWeights-    :: (Vector v (a,Word32), Vector v (a,Double), Vector v a, Vector v Word32, Show a)+    :: (Vector v (a,Word32), Vector v (a,Double), Vector v a, Vector v Word32)        => v (a, Double) -> CondensedTable v a {-# INLINE tableFromWeights #-} tableFromWeights = tableFromProbabilities . normalize . G.filter ((> 0) . snd)

@@ -10,14 +10,17 @@ -- -- Pseudo-random number generation for non-uniform distributions. -module System.Random.MWC.Distributions +module System.Random.MWC.Distributions     (     -- * Variates: non-uniformly distributed values       normal     , standard     , exponential+    , truncatedExp     , gamma     , chiSquare+    , geometric0+    , geometric1      -- * References     -- $references@@ -106,6 +109,21 @@   return $! - log x / beta  +-- | Generate truncated exponentially distributed random variate.+truncatedExp :: PrimMonad m+             => Double            -- ^ Scale parameter+             -> (Double,Double)   -- ^ Range to which distribution is+                                  --   truncated. Values may be negative.+             -> Gen (PrimState m) -- ^ Generator.+             -> m Double+{-# INLINE truncatedExp #-}+truncatedExp beta (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+ -- | Random variate generator for gamma distribution. gamma :: PrimMonad m       => Double                 -- ^ Shape parameter@@ -150,7 +168,32 @@   | otherwise = do x <- gamma (0.5 * fromIntegral n) 1 gen                    return $! 2 * x +-- | Random variate generator for the geometric distribution,+-- computing the number of failures before success. Supports [0..].+geometric0 :: PrimMonad m+           => Double            -- ^ /p/ success probability lies in (0,1]+           -> Gen (PrimState m) -- ^ Generator+           -> m Int+{-# INLINE geometric0 #-}+geometric0 p gen+  | p == 1          = return 0+  | p >  0 && p < 1 = do q <- uniform gen+                         -- FIXME: We want to use log1p here but it will+                         --        introduce dependency on math-functions.+                         return $! floor $ log q / log (1 - p)+  | otherwise       = pkgError "geometric0" "probability out of [0,1] range" +-- | Random variate generator for geometric distribution for number of+-- trials. Supports [1..] (i.e. just 'geometric0' shifted by 1).+geometric1 :: PrimMonad m+           => Double            -- ^ /p/ success probability lies in (0,1]+           -> Gen (PrimState m) -- ^ Generator+           -> m Int+{-# INLINE geometric1 #-}+geometric1 p gen = do n <- geometric0 p gen+                      return $! n + 1++ sqr :: Double -> Double sqr x = x * x {-# INLINE sqr #-}@@ -165,7 +208,7 @@ --   normal random samples. Mimeo, Nuffield College, University of --   Oxford.  <http://www.doornik.com/research/ziggurat.pdf> ----- * Doornik, J.A. (2007) Conversion of high-period random numbers to---   floating point.---   /ACM Transactions on Modeling and Computer Simulation/ 17(1).---   <http://www.doornik.com/research/randomdouble.pdf>+-- * Thomas, D.B.; Leong, P.G.W.; Luk, W.; Villasenor, J.D.+--   (2007). Gaussian random number generators.+--   /ACM Computing Surveys/ 39(4).+--   <http://www.cse.cuhk.edu.hk/~phwl/mt/public/archives/papers/grng_acmcs07.pdf>

@@ -1,5 +1,5 @@ name:           mwc-random-version:        0.12.0.1+version:        0.13.0.0 synopsis:       Fast, high quality pseudo random number generation description:   This package contains code for generating high quality random