Copyright | (c) 2009-2012 Bryan O'Sullivan |
---|---|
License | BSD3 |
Maintainer | bos@serpentine.com |
Stability | experimental |
Portability | portable |
Safe Haskell | None |
Language | Haskell98 |
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
withSystemRandom
call, which creates a random state. - Supply your own seed to
initialize
function. - Finally,
create
makes 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 ->uniformVector
gen 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.
initialize :: (PrimMonad m, Vector v Word32) => v Word32 -> m (Gen (PrimState m)) #
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 xor
ed 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 #
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 #
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 vs
As 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 #
Constrain the type of an action to run in the IO
monad.
asGenST :: (GenST s -> ST s a) -> GenST s -> ST s a #
Constrain the type of an action to run in the ST
monad.
Variates: uniformly distributed values
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.
uniform :: PrimMonad m => Gen (PrimState m) -> m a #
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
log
function).
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 #
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.
Variate Bool # | |
Variate Double # | |
Variate Float # | |
Variate Int # | |
Variate Int8 # | |
Variate Int16 # | |
Variate Int32 # | |
Variate Int64 # | |
Variate Word # | |
Variate Word8 # | |
Variate Word16 # | |
Variate Word32 # | |
Variate Word64 # | |
(Variate a, Variate b) => Variate (a, b) # | |
(Variate a, Variate b, Variate c) => Variate (a, b, c) # | |
(Variate a, Variate b, Variate c, Variate d) => Variate (a, b, c, d) # | |
uniformVector :: (PrimMonad m, Variate a, Vector v a) => Gen (PrimState m) -> Int -> m (v a) #
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
toSeed :: Vector v Word32 => v Word32 -> Seed #
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