| Copyright | (c) 2009-2012 Bryan O'Sullivan | 
|---|---|
| License | BSD3 | 
| Maintainer | bos@serpentine.com | 
| Stability | experimental | 
| Portability | portable | 
| Safe Haskell | None | 
| Language | Haskell98 | 
System.Random.MWC
Contents
Description
Pseudo-random number generation. This module contains code for generating high quality random numbers that follow a uniform distribution.
For non-uniform distributions, see the
 Distributions module.
The uniform PRNG uses Marsaglia's MWC256 (also known as MWC8222) multiply-with-carry generator, which has a period of 2^8222 and fares well in tests of randomness. It is also extremely fast, between 2 and 3 times faster than the Mersenne Twister.
The generator state is stored in the Gen data type. It can be
 created in several ways:
- Using the withSystemRandomcall, which creates a random state.
- Supply your own seed to initializefunction.
- Finally, createmakes a generator from a fixed seed. Generators created in this way aren't really random.
For repeatability, the state of the generator can be snapshotted
 and replayed using the save and restore functions.
The simplest use is to generate a vector of uniformly distributed values:
vs <-withSystemRandom.asGenST$ \gen ->uniformVectorgen 100
These values can be of any type which is an instance of the class
 Variate.
To generate random values on demand, first create a random number
 generator.
  gen <- create
Hold onto this generator and use it wherever random values are
 required (creating a new generator is expensive compared to
 generating a random number, so you don't want to throw them
 away). Get a random value using uniform or uniformR:
  v <- uniform gen
  v <- uniformR (1, 52) gen
- data Gen s
- create :: PrimMonad m => m (Gen (PrimState m))
- initialize :: (PrimMonad m, Vector v Word32) => v Word32 -> m (Gen (PrimState m))
- withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a
- createSystemRandom :: IO GenIO
- type GenIO = Gen (PrimState IO)
- type GenST s = Gen (PrimState (ST s))
- asGenIO :: (GenIO -> IO a) -> GenIO -> IO a
- asGenST :: (GenST s -> ST s a) -> GenST s -> ST s a
- class Variate a where
- uniformVector :: (PrimMonad m, Variate a, Vector v a) => Gen (PrimState m) -> Int -> m (v a)
- data Seed
- fromSeed :: Seed -> Vector Word32
- toSeed :: Vector v Word32 => v Word32 -> Seed
- save :: PrimMonad m => Gen (PrimState m) -> m Seed
- restore :: PrimMonad m => Seed -> m (Gen (PrimState m))
Gen: Pseudo-Random Number Generators
State of the pseudo-random number generator. It uses mutable state so same generator shouldn't be used from the different threads simultaneously.
create :: PrimMonad m => m (Gen (PrimState m)) Source #
Create a generator for variates using a fixed seed.
initialize :: (PrimMonad m, Vector v Word32) => v Word32 -> m (Gen (PrimState m)) Source #
Create a generator for variates using the given seed, of which up to 256 elements will be used. For arrays of less than 256 elements, part of the default seed will be used to finish initializing the generator's state.
Examples:
initialize (singleton 42)
initialize (fromList [4, 8, 15, 16, 23, 42])
If a seed contains fewer than 256 elements, it is first used
 verbatim, then its elements are xored against elements of the
 default seed until 256 elements are reached.
If a seed contains exactly 258 elements, then the last two elements
 are used to set the generator's initial state. This allows for
 complete generator reproducibility, so that e.g. gen' == gen in
 the following example:
gen' <-initialize.fromSeed=<<save
withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a Source #
Seed a PRNG with data from the system's fast source of
 pseudo-random numbers ("/dev/urandom" on Unix-like systems or
 RtlGenRandom on Windows), then run the given action.
This is a somewhat expensive function, and is intended to be called
 only occasionally (e.g. once per thread).  You should use the Gen
 it creates to generate many random numbers.
createSystemRandom :: IO GenIO Source #
Seed a PRNG with data from the system's fast source of pseudo-random
 numbers. All the caveats of withSystemRandom apply here as well.
Type helpers
The functions in this package are deliberately written for
 flexibility, and will run in both the IO and ST monads.
This can defeat the compiler's ability to infer a principal type in simple (and common) cases. For instance, we would like the following to work cleanly:
import System.Random.MWC import Data.Vector.Unboxed main = do v <- withSystemRandom $ \gen -> uniformVector gen 20 print (v :: Vector Int)
Unfortunately, the compiler cannot tell what monad uniformVector
 should execute in.  The "fix" of adding explicit type annotations
 is not pretty:
{-# LANGUAGE ScopedTypeVariables #-}
import Control.Monad.ST
main = do
  vs <- withSystemRandom $
        \(gen::GenST s) -> uniformVector gen 20 :: ST s (Vector Int)
  print vsAs a more readable alternative, this library provides asGenST and
 asGenIO to constrain the types appropriately.  We can get rid of
 the explicit type annotations as follows:
main = do vs <- withSystemRandom . asGenST $ \gen -> uniformVector gen 20 print (vs :: Vector Int)
This is almost as compact as the original code that the compiler rejected.
asGenIO :: (GenIO -> IO a) -> GenIO -> IO a Source #
Constrain the type of an action to run in the IO monad.
asGenST :: (GenST s -> ST s a) -> GenST s -> ST s a Source #
Constrain the type of an action to run in the ST monad.
Variates: uniformly distributed values
class Variate a where Source #
The class of types for which we can generate uniformly distributed random variates.
The uniform PRNG uses Marsaglia's MWC256 (also known as MWC8222) multiply-with-carry generator, which has a period of 2^8222 and fares well in tests of randomness. It is also extremely fast, between 2 and 3 times faster than the Mersenne Twister.
Note: Marsaglia's PRNG is not known to be cryptographically secure, so you should not use it for cryptographic operations.
Methods
uniform :: PrimMonad m => Gen (PrimState m) -> m a Source #
Generate a single uniformly distributed random variate. The range of values produced varies by type:
- For fixed-width integral types, the type's entire range is used.
- For floating point numbers, the range (0,1] is used. Zero is
   explicitly excluded, to allow variates to be used in
   statistical calculations that require non-zero values
   (e.g. uses of the logfunction).
To generate a Float variate with a range of [0,1), subtract
 2**(-33).  To do the same with Double variates, subtract
 2**(-53).
uniformR :: PrimMonad m => (a, a) -> Gen (PrimState m) -> m a Source #
Generate single uniformly distributed random variable in a given range.
- For integral types inclusive range is used.
- For floating point numbers range (a,b] is used if one ignores rounding errors.
Instances
| Variate Bool Source # | |
| Variate Double Source # | |
| Variate Float Source # | |
| Variate Int Source # | |
| Variate Int8 Source # | |
| Variate Int16 Source # | |
| Variate Int32 Source # | |
| Variate Int64 Source # | |
| Variate Word Source # | |
| Variate Word8 Source # | |
| Variate Word16 Source # | |
| Variate Word32 Source # | |
| Variate Word64 Source # | |
| (Variate a, Variate b) => Variate (a, b) Source # | |
| (Variate a, Variate b, Variate c) => Variate (a, b, c) Source # | |
| (Variate a, Variate b, Variate c, Variate d) => Variate (a, b, c, d) Source # | |
uniformVector :: (PrimMonad m, Variate a, Vector v a) => Gen (PrimState m) -> Int -> m (v a) Source #
Generate a vector of pseudo-random variates.  This is not
 necessarily faster than invoking uniform repeatedly in a loop,
 but it may be more convenient to use in some situations.
Seed: state management
An immutable snapshot of the state of a Gen.
toSeed :: Vector v Word32 => v Word32 -> Seed Source #
Convert vector to Seed. It acts similarily to initialize and
 will accept any vector. If you want to pass seed immediately to
 restore you better call initialize directly since following law holds:
restore (toSeed v) = initialize v
References
- Marsaglia, G. (2003) Seeds for random number generators. Communications of the ACM 46(5):90–93. http://doi.acm.org/10.1145/769800.769827
- 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