mwc-random-0.15.0.2: Fast, high quality pseudo random number generation

System.Random.MWC

Description

Pseudo-random number generation using 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. There're two representation of generator: Gen which is generator that uses in-place mutation and Seed which is immutable snapshot of generator's state.

## Initialization

Generator could be initialized in several ways. One is to obtain randomness from operating system using createSystemRandom, createSystemSeed or withSystemRandomST (All examples assume that System.Random.Stateful is imported)

>>> g <- createSystemRandom
>>> uniformM g :: IO Int
...

>>> withSystemRandomST $\g -> uniformM g :: IO Int ...  Deterministically create generator from given seed using initialize function: >>> import Data.Int >>> import qualified Data.Vector.Unboxed as U >>> import System.Random.Stateful >>> g <- initialize$ U.fromList [1,2,3]
>>> uniformRM (1,200) g :: IO Int64
101


Last way is to create generator with fixed seed which could be useful in testing

>>> g <- create
>>> uniformM g :: IO Int
-8765701622605876598


## Generation of random numbers

Recommended way of generating random numbers in simple cases like generating uniformly distributed random number in range or value uniformly distributed in complete type domain is to use UniformRange and Uniform type classes. Note that while small self-contained examples usually require explicit annotations usually result type could be inferred.

This example simulates 20 throws of fair 6-sided dice:

>>> g <- create
>>> replicateM 20 $uniformRM (1, 6::Integer) g [3,4,3,1,4,6,1,6,1,4,2,2,3,2,4,2,5,1,3,5]  For generating full range of possible values one could use uniformM. This example generates 10 random bytes, or equivalently 10 throws of 256-sided dice: >>> g <- create >>> replicateM 10$ uniformM g :: IO [Word8]
[209,138,126,150,165,15,69,203,155,146]


There're special functions for generation of Doubles and @Float in unit interval: uniformDouble01M, uniformDoublePositive01M, uniformFloat01M, uniformFloatPositive01M:

>>> uniformDouble01M =<< create
0.5248103628705498
>>> uniformFloat01M =<< create
0.5248104


For normal distribution and others see modules System.Random.MWC.Distributions and System.Random.MWC.CondensedTable. Note that they could be used with any other generator implementing StatefulGen API

There're special cases for generating random vectors and bytestrings. For example in order to generate random 10-byte sequences as unboxed vector or bytestring:

>>> g <- create
>>> uniformVector g 10 :: IO (U.Vector Word8)
[209,138,126,150,165,15,69,203,155,146]

>>> import qualified Data.ByteString as BS
>>> g <- create
>>> BS.unpack <$> uniformByteStringM 10 g [138,242,130,33,209,248,89,134,150,180]  Note that uniformByteStringM produces different result from uniformVector since it uses PRNG's output more efficently. ## State handling For repeatability, the state of the generator can be snapshotted and replayed using the save and restore functions. Following example shows how to save and restore generator: >>> g <- create >>> replicateM_ 10 (uniformM g :: IO Word64) >>> s <- save g >>> uniformM g :: IO Word32 1771812561 >>> uniformM =<< restore s :: IO Word32 1771812561  Synopsis # Gen: Pseudo-Random Number Generators data Gen s Source # State of the pseudo-random number generator. It uses mutable state so same generator shouldn't be used from the different threads simultaneously. #### Instances Instances details  (s ~ PrimState m, PrimMonad m) => StatefulGen (Gen s) m Source # Since: 0.15.0.0 Instance detailsDefined in System.Random.MWC MethodsuniformWord32R :: Word32 -> Gen s -> m Word32 #uniformWord64R :: Word64 -> Gen s -> m Word64 #uniformWord8 :: Gen s -> m Word8 #uniformWord16 :: Gen s -> m Word16 #uniformWord32 :: Gen s -> m Word32 #uniformWord64 :: Gen s -> m Word64 # 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 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. Generate random seed for generator using system's fast source of pseudo-random numbers. Since: 0.15.0.0 Seed a PRNG with data from the system's fast source of pseudo-random numbers. withSystemRandomST :: (forall s. Gen s -> ST s a) -> IO a Source # Seed PRNG with data from the system's fast source of pseudo-random numbers and execute computation in ST monad. Since: 0.15.0.0 ## 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 #-}

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. type GenIO = Gen (PrimState IO) Source # A shorter name for PRNG state in the IO monad. type GenST s = Gen (PrimState (ST s)) Source # A shorter name for PRNG state in the ST monad. 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 Uniform a where # The class of types for which a uniformly distributed value can be drawn from all possible values of the type. Since: random-1.2.0 Methods uniformM :: StatefulGen g m => g -> m a # Generates a value uniformly distributed over all possible values of that type. Since: random-1.2.0 #### Instances Instances details  Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Bool # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Char # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Int # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Int8 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Int16 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Int32 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Int64 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Word # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Word8 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Word16 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Word32 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m Word64 # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CChar # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CSChar # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CUChar # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CShort # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CUShort # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CInt # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CUInt # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CLong # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CULong # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CLLong # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CULLong # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CBool # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CPtrdiff # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CSize # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CWchar # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CSigAtomic # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CIntPtr # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CUIntPtr # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CIntMax # Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m CUIntMax # (Uniform a, Uniform b) => Uniform (a, b) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m (a, b) # (Uniform a, Uniform b, Uniform c) => Uniform (a, b, c) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m (a, b, c) # (Uniform a, Uniform b, Uniform c, Uniform d) => Uniform (a, b, c, d) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m (a, b, c, d) # (Uniform a, Uniform b, Uniform c, Uniform d, Uniform e) => Uniform (a, b, c, d, e) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m (a, b, c, d, e) # (Uniform a, Uniform b, Uniform c, Uniform d, Uniform e, Uniform f) => Uniform (a, b, c, d, e, f) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g m => g -> m (a, b, c, d, e, f) # (Uniform a, Uniform b, Uniform c, Uniform d, Uniform e, Uniform f, Uniform g) => Uniform (a, b, c, d, e, f, g) Instance detailsDefined in System.Random.Internal MethodsuniformM :: StatefulGen g0 m => g0 -> m (a, b, c, d, e, f, g) # class UniformRange a where # The class of types for which a uniformly distributed value can be drawn from a range. Since: random-1.2.0 Methods uniformRM :: StatefulGen g m => (a, a) -> g -> m a # Generates a value uniformly distributed over the provided range, which is interpreted as inclusive in the lower and upper bound. • uniformRM (1 :: Int, 4 :: Int) generates values uniformly from the set \(\{1,2,3,4\}$$ • uniformRM (1 :: Float, 4 :: Float) generates values uniformly from the set $$\{x\;|\;1 \le x \le 4\}$$ The following law should hold to make the function always defined: uniformRM (a, b) = uniformRM (b, a) Since: random-1.2.0 #### Instances Instances details  Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Bool, Bool) -> g -> m Bool # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Char, Char) -> g -> m Char # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Double, Double) -> g -> m Double # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Float, Float) -> g -> m Float # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Int, Int) -> g -> m Int # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Int8, Int8) -> g -> m Int8 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Int16, Int16) -> g -> m Int16 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Int32, Int32) -> g -> m Int32 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Int64, Int64) -> g -> m Int64 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Integer, Integer) -> g -> m Integer # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Natural, Natural) -> g -> m Natural # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Word, Word) -> g -> m Word # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Word8, Word8) -> g -> m Word8 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Word16, Word16) -> g -> m Word16 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Word32, Word32) -> g -> m Word32 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (Word64, Word64) -> g -> m Word64 # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CChar, CChar) -> g -> m CChar # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CSChar, CSChar) -> g -> m CSChar # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CUChar, CUChar) -> g -> m CUChar # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CShort, CShort) -> g -> m CShort # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CUShort, CUShort) -> g -> m CUShort # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CInt, CInt) -> g -> m CInt # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CUInt, CUInt) -> g -> m CUInt # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CLong, CLong) -> g -> m CLong # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CULong, CULong) -> g -> m CULong # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CLLong, CLLong) -> g -> m CLLong # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CULLong, CULLong) -> g -> m CULLong # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CBool, CBool) -> g -> m CBool # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CFloat, CFloat) -> g -> m CFloat # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CDouble, CDouble) -> g -> m CDouble # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CPtrdiff, CPtrdiff) -> g -> m CPtrdiff # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CSize, CSize) -> g -> m CSize # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CWchar, CWchar) -> g -> m CWchar # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CSigAtomic, CSigAtomic) -> g -> m CSigAtomic # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CIntPtr, CIntPtr) -> g -> m CIntPtr # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CUIntPtr, CUIntPtr) -> g -> m CUIntPtr # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CIntMax, CIntMax) -> g -> m CIntMax # Instance detailsDefined in System.Random.Internal MethodsuniformRM :: StatefulGen g m => (CUIntMax, CUIntMax) -> g -> m CUIntMax # class Variate a where Source # NOTE: Consider use of more principled type classes Uniform and UniformRange instead. 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 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 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 Instances details  Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Bool Source #uniformR :: PrimMonad m => (Bool, Bool) -> Gen (PrimState m) -> m Bool Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Double Source #uniformR :: PrimMonad m => (Double, Double) -> Gen (PrimState m) -> m Double Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Float Source #uniformR :: PrimMonad m => (Float, Float) -> Gen (PrimState m) -> m Float Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Int Source #uniformR :: PrimMonad m => (Int, Int) -> Gen (PrimState m) -> m Int Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Int8 Source #uniformR :: PrimMonad m => (Int8, Int8) -> Gen (PrimState m) -> m Int8 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Int16 Source #uniformR :: PrimMonad m => (Int16, Int16) -> Gen (PrimState m) -> m Int16 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Int32 Source #uniformR :: PrimMonad m => (Int32, Int32) -> Gen (PrimState m) -> m Int32 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Int64 Source #uniformR :: PrimMonad m => (Int64, Int64) -> Gen (PrimState m) -> m Int64 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Word Source #uniformR :: PrimMonad m => (Word, Word) -> Gen (PrimState m) -> m Word Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Word8 Source #uniformR :: PrimMonad m => (Word8, Word8) -> Gen (PrimState m) -> m Word8 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Word16 Source #uniformR :: PrimMonad m => (Word16, Word16) -> Gen (PrimState m) -> m Word16 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Word32 Source #uniformR :: PrimMonad m => (Word32, Word32) -> Gen (PrimState m) -> m Word32 Source # Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m Word64 Source #uniformR :: PrimMonad m => (Word64, Word64) -> Gen (PrimState m) -> m Word64 Source # (Variate a, Variate b) => Variate (a, b) Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m (a, b) Source #uniformR :: PrimMonad m => ((a, b), (a, b)) -> Gen (PrimState m) -> m (a, b) Source # (Variate a, Variate b, Variate c) => Variate (a, b, c) Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m (a, b, c) Source #uniformR :: PrimMonad m => ((a, b, c), (a, b, c)) -> Gen (PrimState m) -> m (a, b, c) Source # (Variate a, Variate b, Variate c, Variate d) => Variate (a, b, c, d) Source # Instance detailsDefined in System.Random.MWC Methodsuniform :: PrimMonad m => Gen (PrimState m) -> m (a, b, c, d) Source #uniformR :: PrimMonad m => ((a, b, c, d), (a, b, c, d)) -> Gen (PrimState m) -> m (a, b, c, d) Source # uniformVector :: (PrimMonad m, StatefulGen g m, Uniform a, Vector v a) => g -> 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 data Seed Source # An immutable snapshot of the state of a Gen. #### Instances Instances details  Source # Instance detailsDefined in System.Random.MWC Methods(==) :: Seed -> Seed -> Bool #(/=) :: Seed -> Seed -> Bool # Source # Instance detailsDefined in System.Random.MWC MethodsshowsPrec :: Int -> Seed -> ShowS #show :: Seed -> String #showList :: [Seed] -> ShowS # Source # Since: 0.15.0.0 Instance detailsDefined in System.Random.MWC Associated Typestype MutableGen Seed m = (g :: Type) # MethodsfreezeGen :: MutableGen Seed m -> m Seed #thawGen :: Seed -> m (MutableGen Seed m) # type MutableGen Seed m Source # Instance detailsDefined in System.Random.MWC type MutableGen Seed m = Gen (PrimState m) Convert seed into vector. 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 save :: PrimMonad m => Gen (PrimState m) -> m Seed Source # Save the state of a Gen, for later use by restore. restore :: PrimMonad m => Seed -> m (Gen (PrimState m)) Source # Create a new Gen that mirrors the state of a saved Seed. # Deprecated withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a Source # Deprecated: Use withSystemRandomST or createSystemSeed or createSystemRandom instead Seed a PRNG with data from the system's fast source of pseudo-random numbers, then run the given action. This function is unsafe and for example allows STRefs or any other mutable data structure to escape scope: >>> ref <- withSystemRandom$ \_ -> newSTRef 1
>>> withSystemRandom $\_ -> modifySTRef ref succ >> readSTRef ref 2 >>> withSystemRandom$ \_ -> modifySTRef ref succ >> readSTRef ref
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