| Copyright | [2014..2020] Trevor L. McDonell |
|---|---|
| License | BSD3 |
| Maintainer | Trevor L. McDonell <trevor.mcdonell@gmail.com> |
| Stability | experimental |
| Portability | non-portable (GHC extensions) |
| Safe Haskell | None |
| Language | Haskell2010 |
Data.Array.Accelerate.System.Random.MWC
Contents
Description
Random number generation backed by MWC.
- Example
Create a vector of 100 random uniformly distributed floating-point numbers,
where the PRNG is seeded with data from the system's source of pseudo-random
numbers (see withSystemRandom):
>>>vs <- randomArray uniform (Z :. 100) :: IO (Vector Float)
To generate uniformly distributed random variables in the range (-1,1]:
>>>vs <- randomArray (uniformR (-1,1)) (Z:.100) :: IO (Vector Double)
You can also pass the generator state in explicitly, so that it can be reused:
>>>gen <- create :: IO GenIO>>>vs <- randomArrayWith gen uniform (Z :. 100) :: IO (Vector Int)
- Non-uniform distributions
If you require random numbers following other distributions, you can combine this package with the generators from the random-fu package. For example:
import Data.Random hiding ( uniform )
import qualified Data.Random.Distribution.Exponential as R
import qualified Data.Random.Distribution.Poisson as R
exponential
:: (Distribution StdUniform e, Floating e, Shape sh, Elt e)
=> e
-> sh :~> e
exponential beta _sh gen = sampleFrom gen (R.exponential beta)
poisson
:: (Distribution (R.Poisson b) a, Shape sh, Elt a)
=> b
-> sh :~> a
poisson lambda _sh gen = sampleFrom gen (R.poisson lambda)
Which can then be used as before:
>>>vs <- randomArray (exponential 5) (Z :. 100) :: IO (Vector Float)>>>us <- randomArray (poisson 5) (Z :. 100) :: IO (Vector Float)
Synopsis
- type (:~>) sh e = sh -> GenIO -> IO e
- uniform :: (Shape sh, Elt e, Variate e) => sh :~> e
- uniformR :: (Shape sh, Elt e, Variate e) => (e, e) -> sh :~> e
- randomArray :: (Shape sh, Elt e) => (sh :~> e) -> sh -> IO (Array sh e)
- randomArrayWith :: (Shape sh, Elt e) => GenIO -> (sh :~> e) -> sh -> IO (Array sh e)
- uniformVector :: (PrimMonad m, Variate a, Vector v a) => Gen (PrimState m) -> Int -> m (v a)
- createSystemRandom :: IO GenIO
- withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a
- restore :: PrimMonad m => Seed -> m (Gen (PrimState m))
- save :: PrimMonad m => Gen (PrimState m) -> m Seed
- toSeed :: Vector v Word32 => v Word32 -> Seed
- initialize :: (PrimMonad m, Vector v Word32) => v Word32 -> m (Gen (PrimState m))
- create :: PrimMonad m => m (Gen (PrimState m))
- asGenST :: (GenST s -> ST s a) -> GenST s -> ST s a
- asGenIO :: (GenIO -> IO a) -> GenIO -> IO a
- class Variate a
- data Gen s
- type GenIO = Gen (PrimState IO)
- type GenST s = Gen (PrimState (ST s))
- data Seed
Generating random arrays
uniformR :: (Shape sh, Elt e, Variate e) => (e, e) -> sh :~> e Source #
Uniformly distributed random variates in a given range.
randomArray :: (Shape sh, Elt e) => (sh :~> e) -> sh -> IO (Array sh e) Source #
Generate an array of random values. The generator for variates is
seeded from the system's fast source of pseudo-random numbers (see:
createSystemRandom)
randomArrayWith :: (Shape sh, Elt e) => GenIO -> (sh :~> e) -> sh -> IO (Array sh e) Source #
Generate an array of random values using the supplied generator.
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.
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.
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.
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
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 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
In the MWC algorithm, the carry value must be strictly smaller than the multiplicator (see https://en.wikipedia.org/wiki/Multiply-with-carry). Hence, if a seed contains exactly 258 elements, the carry value, which is the last of the 258 values, is moduloed by the multiplicator.
Note that if the first carry value is strictly smaller than the multiplicator,
all subsequent carry values are also strictly smaller than the multiplicator
(a proof of this is in the comments of the code of uniformWord32), hence
when restoring a saved state, we have the guarantee that moduloing the saved
carry won't modify its value.
asGenST :: (GenST s -> ST s a) -> GenST s -> ST s a #
Constrain the type of an action to run in the ST monad.
asGenIO :: (GenIO -> IO a) -> GenIO -> IO a #
Constrain the type of an action to run in the IO monad.
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.
Instances
| 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) | |
State of the pseudo-random number generator. It uses mutable state so same generator shouldn't be used from the different threads simultaneously.
An immutable snapshot of the state of a Gen.