-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/
-- | Pseudo-random number generation
--
-- This package provides basic pseudo-random number generation, including
-- the ability to split random number generators.
--
-- System.Random: pure pseudo-random number interface
--
-- In pure code, use System.Random.uniform and
-- System.Random.uniformR from System.Random to generate
-- pseudo-random numbers with a pure pseudo-random number generator like
-- System.Random.StdGen.
--
-- As an example, here is how you can simulate rolls of a six-sided die
-- using System.Random.uniformR:
--
--
-- >>> let roll = uniformR (1, 6) :: RandomGen g => g -> (Word, g)
--
-- >>> let rolls = unfoldr (Just . roll) :: RandomGen g => g -> [Word]
--
-- >>> let pureGen = mkStdGen 42
--
-- >>> take 10 (rolls pureGen) :: [Word]
-- [1,1,3,2,4,5,3,4,6,2]
--
--
-- See System.Random for more details.
--
--
--
-- In monadic code, use System.Random.Stateful.uniformM and
-- System.Random.Stateful.uniformRM from
-- System.Random.Stateful to generate pseudo-random numbers with a
-- monadic pseudo-random number generator, or using a monadic adapter.
--
-- As an example, here is how you can simulate rolls of a six-sided die
-- using System.Random.Stateful.uniformRM:
--
--
-- >>> let rollM = uniformRM (1, 6) :: StatefulGen g m => g -> m Word
--
-- >>> let pureGen = mkStdGen 42
--
-- >>> runStateGen_ pureGen (replicateM 10 . rollM) :: [Word]
-- [1,1,3,2,4,5,3,4,6,2]
--
--
-- The monadic adapter System.Random.Stateful.runStateGen_ is used
-- here to lift the pure pseudo-random number generator pureGen
-- into the System.Random.Stateful.StatefulGen context.
--
-- The monadic interface can also be used with existing monadic
-- pseudo-random number generators. In this example, we use the one
-- provided in the mwc-random package:
--
--
-- >>> import System.Random.MWC as MWC
--
-- >>> let rollM = uniformRM (1, 6) :: StatefulGen g m => g -> m Word
--
-- >>> monadicGen <- MWC.create
--
-- >>> replicateM 10 (rollM monadicGen) :: IO [Word]
-- [2,3,6,6,4,4,3,1,5,4]
--
--
-- See System.Random.Stateful for more details.
@package random
@version 1.3.0
-- | This library deals with the common task of pseudo-random number
-- generation.
module System.Random
-- | RandomGen is an interface to pure pseudo-random number
-- generators.
--
-- StdGen is the standard RandomGen instance provided by
-- this library.
class RandomGen g
-- | Returns a Word8 that is uniformly distributed over the entire
-- Word8 range.
genWord8 :: RandomGen g => g -> (Word8, g)
-- | Returns a Word16 that is uniformly distributed over the entire
-- Word16 range.
genWord16 :: RandomGen g => g -> (Word16, g)
-- | Returns a Word32 that is uniformly distributed over the entire
-- Word32 range.
genWord32 :: RandomGen g => g -> (Word32, g)
-- | Returns a Word64 that is uniformly distributed over the entire
-- Word64 range.
genWord64 :: RandomGen g => g -> (Word64, g)
-- | genWord32R upperBound g returns a Word32 that is
-- uniformly distributed over the range [0, upperBound].
genWord32R :: RandomGen g => Word32 -> g -> (Word32, g)
-- | genWord64R upperBound g returns a Word64 that is
-- uniformly distributed over the range [0, upperBound].
genWord64R :: RandomGen g => Word64 -> g -> (Word64, g)
-- | Fill in the supplied MutableByteArray with uniformly generated
-- random bytes. This function is unsafe because it is not required to do
-- any bounds checking. For a safe variant use
-- uniformFillMutableByteArrayM instead.
--
-- Default type class implementation uses
-- defaultUnsafeUniformFillMutableByteArray.
unsafeUniformFillMutableByteArray :: RandomGen g => MutableByteArray s -> Int -> Int -> g -> ST s g
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated. Some pseudo-random number generators
-- are not splittable. In that case, the split implementation
-- should fail with a descriptive error message.
-- | Deprecated: In favor of splitGen
split :: RandomGen g => g -> (g, g)
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated. Some pseudo-random number generators
-- are not splittable. In that case, the split implementation
-- should fail with a descriptive error message.
-- | Deprecated: In favor of splitGen
split :: (RandomGen g, SplitGen g) => g -> (g, g)
-- | Pseudo-random generators that can be split into two separate and
-- independent psuedo-random generators should provide an instance for
-- this type class.
--
-- Historically this functionality was included in the RandomGen
-- type class in the split function, however, few pseudo-random
-- generators possess this property of splittability. This lead the old
-- split function being usually implemented in terms of
-- error.
class RandomGen g => SplitGen g
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated.
splitGen :: SplitGen g => g -> (g, g)
-- | Generates a value uniformly distributed over all possible values of
-- that type.
--
-- This is a pure version of uniformM.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> uniform pureGen :: (Bool, StdGen)
-- (True,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> uniform @Bool pureGen
-- (True,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
uniform :: (Uniform a, RandomGen g) => g -> (a, g)
-- | Generates a value uniformly distributed over the provided range, which
-- is interpreted as inclusive in the lower and upper bound.
--
--
-- - uniformR (1 :: Int, 4 :: Int) generates values uniformly
-- from the set <math>
-- - uniformR (1 :: Float, 4 :: Float) generates values
-- uniformly from the set <math>
--
--
-- The following law should hold to make the function always defined:
--
--
-- uniformR (a, b) = uniformR (b, a)
--
--
-- This is a pure version of uniformRM.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> uniformR (1 :: Int, 4 :: Int) pureGen
-- (4,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> uniformR @Int (1, 4) pureGen
-- (4,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
uniformR :: (UniformRange a, RandomGen g) => (a, a) -> g -> (a, g)
-- | The class of types for which random values can be generated. Most
-- instances of Random will produce values that are uniformly
-- distributed on the full range, but for those types without a
-- well-defined "full range" some sensible default subrange will be
-- selected.
--
-- Random exists primarily for backwards compatibility with
-- version 1.1 of this library. In new code, use the better specified
-- Uniform and UniformRange instead.
class Random a
-- | Takes a range (lo,hi) and a pseudo-random number generator
-- g, and returns a pseudo-random value uniformly distributed over
-- the closed interval [lo,hi], together with a new generator. It
-- is unspecified what happens if lo>hi, but usually the values
-- will simply get swapped.
--
--
-- >>> let gen = mkStdGen 26
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
--
-- For continuous types there is no requirement that the values lo
-- and hi are ever produced, but they may be, depending on the
-- implementation and the interval.
--
-- There is no requirement to follow the Ord instance and the
-- concept of range can be defined on per type basis. For example product
-- types will treat their values independently:
--
--
-- >>> fst $ randomR (('a', 5.0), ('z', 10.0)) $ mkStdGen 26
-- ('z',5.22694980853051)
--
--
-- In case when a lawful range is desired uniformR should be used
-- instead.
randomR :: (Random a, RandomGen g) => (a, a) -> g -> (a, g)
-- | Takes a range (lo,hi) and a pseudo-random number generator
-- g, and returns a pseudo-random value uniformly distributed over
-- the closed interval [lo,hi], together with a new generator. It
-- is unspecified what happens if lo>hi, but usually the values
-- will simply get swapped.
--
--
-- >>> let gen = mkStdGen 26
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
--
-- For continuous types there is no requirement that the values lo
-- and hi are ever produced, but they may be, depending on the
-- implementation and the interval.
--
-- There is no requirement to follow the Ord instance and the
-- concept of range can be defined on per type basis. For example product
-- types will treat their values independently:
--
--
-- >>> fst $ randomR (('a', 5.0), ('z', 10.0)) $ mkStdGen 26
-- ('z',5.22694980853051)
--
--
-- In case when a lawful range is desired uniformR should be used
-- instead.
randomR :: (Random a, RandomGen g, UniformRange a) => (a, a) -> g -> (a, g)
-- | The same as randomR, but using a default range determined by
-- the type:
--
--
-- - For bounded types (instances of Bounded, such as
-- Char), the range is normally the whole type.
-- - For floating point types, the range is normally the closed
-- interval [0,1].
-- - For Integer, the range is (arbitrarily) the range of
-- Int.
--
random :: (Random a, RandomGen g) => g -> (a, g)
-- | The same as randomR, but using a default range determined by
-- the type:
--
--
-- - For bounded types (instances of Bounded, such as
-- Char), the range is normally the whole type.
-- - For floating point types, the range is normally the closed
-- interval [0,1].
-- - For Integer, the range is (arbitrarily) the range of
-- Int.
--
random :: (Random a, RandomGen g, Uniform a) => g -> (a, g)
-- | Plural variant of randomR, producing an infinite list of
-- pseudo-random values instead of returning a new generator.
randomRs :: (Random a, RandomGen g) => (a, a) -> g -> [a]
-- | Plural variant of random, producing an infinite list of
-- pseudo-random values instead of returning a new generator.
randoms :: (Random a, RandomGen g) => g -> [a]
-- | The class of types for which a uniformly distributed value can be
-- drawn from all possible values of the type.
class Uniform a
-- | The class of types for which a uniformly distributed value can be
-- drawn from a range.
class UniformRange a
-- | A type class for data with a finite number of inhabitants. This type
-- class is used in the default implementation of Uniform.
--
-- Users are not supposed to write instances of Finite manually.
-- There is a default implementation in terms of Generic instead.
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> data MyBool = MyTrue | MyFalse deriving (Generic, Finite)
--
-- >>> data Action = Code MyBool | Eat (Maybe Bool) | Sleep deriving (Generic, Finite)
--
class Finite a
-- | Interface for converting a pure pseudo-random number generator to and
-- from non-empty sequence of bytes. Seeds are stored in Little-Endian
-- order regardless of the platform it is being used on, which provides
-- cross-platform compatibility, while providing optimal performance for
-- the most common platform type.
--
-- Conversion to and from a Seed serves as a building block for
-- implementing serialization for any pure or frozen pseudo-random number
-- generator.
--
-- It is not trivial to implement platform independence. For this reason
-- this type class has two alternative ways of creating an instance for
-- this class. The easiest way for constructing a platform indepent seed
-- is by converting the inner state of a generator to and from a list of
-- 64 bit words using toSeed64 and fromSeed64 respectively.
-- In that case cross-platform support will be handled automaticaly.
--
--
-- >>> :set -XDataKinds -XTypeFamilies
--
-- >>> import Data.Word (Word8, Word32)
--
-- >>> import Data.Bits ((.|.), shiftR, shiftL)
--
-- >>> import Data.List.NonEmpty (NonEmpty ((:|)))
--
-- >>> data FiveByteGen = FiveByteGen Word8 Word32 deriving Show
--
-- >>> :{
-- instance SeedGen FiveByteGen where
-- type SeedSize FiveByteGen = 5
-- fromSeed64 (w64 :| _) =
-- FiveByteGen (fromIntegral (w64 `shiftR` 32)) (fromIntegral w64)
-- toSeed64 (FiveByteGen x1 x4) =
-- let w64 = (fromIntegral x1 `shiftL` 32) .|. fromIntegral x4
-- in (w64 :| [])
-- :}
--
--
--
-- >>> FiveByteGen 0x80 0x01020304
-- FiveByteGen 128 16909060
--
-- >>> fromSeed (toSeed (FiveByteGen 0x80 0x01020304))
-- FiveByteGen 128 16909060
--
-- >>> toSeed (FiveByteGen 0x80 0x01020304)
-- Seed [0x04, 0x03, 0x02, 0x01, 0x80]
--
-- >>> toSeed64 (FiveByteGen 0x80 0x01020304)
-- 549772722948 :| []
--
--
-- However, when performance is of utmost importance or default handling
-- of cross platform independence is not sufficient, then an adventurous
-- developer can try implementing conversion into bytes directly with
-- toSeed and fromSeed.
--
-- Properties that must hold:
--
--
-- > fromSeed (toSeed gen) == gen
--
--
--
-- > fromSeed64 (toSeed64 gen) == gen
--
--
-- Note, that there is no requirement for every Seed to roundtrip,
-- eg. this proprty does not even hold for StdGen:
--
--
-- >>> let seed = nonEmptyToSeed (0xab :| [0xff00]) :: Seed StdGen
--
-- >>> seed == toSeed (fromSeed seed)
-- False
--
class (KnownNat (SeedSize g), 1 <= SeedSize g, Typeable g) => SeedGen g where {
-- | Number of bytes that is required for storing the full state of a
-- pseudo-random number generator. It should be big enough to satisfy the
-- roundtrip property:
--
--
-- > fromSeed (toSeed gen) == gen
--
type SeedSize g :: Nat;
}
-- | Convert from a binary representation to a pseudo-random number
-- generator
fromSeed :: SeedGen g => Seed g -> g
-- | Convert to a binary representation of a pseudo-random number generator
toSeed :: SeedGen g => g -> Seed g
-- | Construct pseudo-random number generator from a list of words.
-- Whenever list does not have enough bytes to satisfy the
-- SeedSize requirement, it will be padded with zeros. On the
-- other hand when it has more than necessary, extra bytes will be
-- dropped.
--
-- For example if SeedSize is set to 2, then only the lower 16
-- bits of the first element in the list will be used.
fromSeed64 :: SeedGen g => NonEmpty Word64 -> g
-- | Convert pseudo-random number generator to a list of words
--
-- In case when SeedSize is not a multiple of 8, then the upper
-- bits of the last word in the list will be set to zero.
toSeed64 :: SeedGen g => g -> NonEmpty Word64
-- | This is a binary form of pseudo-random number generator's state. It is
-- designed to be safe and easy to use for input/output operations like
-- restoring from file, transmitting over the network, etc.
--
-- Constructor is not exported, becasue it is important for
-- implementation to enforce the invariant of the underlying byte array
-- being of the exact same length as the generator has specified in
-- SeedSize. Use mkSize and unSize to get access to
-- the raw bytes in a safe manner.
data Seed g
-- | Get the expected size of the Seed in number bytes
seedSize :: forall g. SeedGen g => Int
-- | Just like seedSize, except it accepts a proxy as an argument.
seedSizeProxy :: forall proxy g. SeedGen g => proxy g -> Int
-- | Construct a Seed from a ByteArray of expected length.
-- Whenever ByteArray does not match the SeedSize specified
-- by the pseudo-random generator, this function will fail.
mkSeed :: forall g m. (SeedGen g, MonadFail m) => ByteArray -> m (Seed g)
-- | Unwrap the Seed and get the underlying ByteArray
unSeed :: Seed g -> ByteArray
-- | Just like mkSeed, but uses ByteString as argument.
-- Results in a memcopy of the seed.
mkSeedFromByteString :: (SeedGen g, MonadFail m) => ByteString -> m (Seed g)
-- | Just like unSeed, but produced a ByteString. Results
-- in a memcopy of the seed.
unSeedToByteString :: Seed g -> ByteString
-- | Helper function that allows for operating directly on the Seed,
-- while supplying a function that uses the pseudo-random number
-- generator that is constructed from that Seed.
--
-- Example
--
--
-- >>> :set -XTypeApplications
--
-- >>> import System.Random
--
-- >>> withSeed (nonEmptyToSeed (pure 2024) :: Seed StdGen) (uniform @Int)
-- (1039666877624726199,Seed [0xe9, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00])
--
withSeed :: SeedGen g => Seed g -> (g -> (a, g)) -> (a, Seed g)
-- | Same as withSeed, except it is useful with monadic computation
-- and frozen generators.
--
-- See withSeedMutableGen for a helper that also handles seeds for
-- mutable pseduo-random number generators.
withSeedM :: (SeedGen g, Functor f) => Seed g -> (g -> f (a, g)) -> f (a, Seed g)
-- | Read the seed from a file and use it for constructing a pseudo-random
-- number generator. After supplied action has been applied to the
-- constructed generator, the resulting generator will be converted back
-- to a seed and written to the same file.
withSeedFile :: (SeedGen g, MonadIO m) => FilePath -> (Seed g -> m (a, Seed g)) -> m a
-- | This is a function that shows the name of the generator type, which is
-- useful for error reporting.
seedGenTypeName :: forall g. SeedGen g => String
-- | Construct a seed from a list of 64-bit words. At most SeedSize
-- many bytes will be used.
nonEmptyToSeed :: forall g. SeedGen g => NonEmpty Word64 -> Seed g
-- | Convert a Seed to a list of 64bit words.
nonEmptyFromSeed :: forall g. SeedGen g => Seed g -> NonEmpty Word64
-- | Produce an infinite list of pseudo-random values. Integrates nicely
-- with list fusion. Naturally, there is no way to recover the final
-- generator, therefore either use split before calling
-- uniforms or use uniformList instead.
--
-- Similar to randoms, except it relies on Uniform type
-- class instead of Random
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> import Data.Word (Word16)
--
-- >>> take 5 $ uniforms gen :: [Word16]
-- [56342,15850,25292,14347,13919]
--
uniforms :: (Uniform a, RandomGen g) => g -> [a]
-- | Produce an infinite list of pseudo-random values in a specified range.
-- Same as uniforms, integrates nicely with list fusion. There is
-- no way to recover the final generator, therefore either use
-- split before calling uniformRs or use
-- uniformListR instead.
--
-- Similar to randomRs, except it relies on UniformRange
-- type class instead of Random.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> take 5 $ uniformRs (10, 100) gen :: [Int]
-- [32,86,21,57,39]
--
uniformRs :: (UniformRange a, RandomGen g) => (a, a) -> g -> [a]
-- | Produce a list of the supplied length with elements generated
-- uniformly.
--
-- See uniformListM for a stateful counterpart.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> import Data.Word (Word16)
--
-- >>> uniformList 5 gen :: ([Word16], StdGen)
-- ([56342,15850,25292,14347,13919],StdGen {unStdGen = SMGen 6446154349414395371 1920468677557965761})
--
uniformList :: (Uniform a, RandomGen g) => Int -> g -> ([a], g)
-- | Produce a list of the supplied length with elements generated
-- uniformly.
--
-- See uniformListM for a stateful counterpart.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> uniformListR 10 (20, 30) gen :: ([Int], StdGen)
-- ([26,30,27,24,30,25,27,21,27,27],StdGen {unStdGen = SMGen 12965503083958398648 1920468677557965761})
--
uniformListR :: (UniformRange a, RandomGen g) => Int -> (a, a) -> g -> ([a], g)
-- | Shuffle elements of a list in a uniformly random order.
--
-- Examples
--
--
-- >>> uniformShuffleList "ELVIS" $ mkStdGen 252
-- ("LIVES",StdGen {unStdGen = SMGen 17676540583805057877 5302934877338729551})
--
uniformShuffleList :: RandomGen g => [a] -> g -> ([a], g)
-- | Efficiently generates a sequence of pseudo-random bytes in a platform
-- independent manner.
uniformByteArray :: RandomGen g => Bool -> Int -> g -> (ByteArray, g)
-- | Generates a ByteString of the specified size using a pure
-- pseudo-random number generator. See uniformByteStringM for
-- the monadic version.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> import Data.ByteString (unpack)
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> unpack . fst $ uniformByteString 10 pureGen
-- [51,123,251,37,49,167,90,109,1,4]
--
uniformByteString :: RandomGen g => Int -> g -> (ByteString, g)
-- | Same as uniformByteArray False, but for
-- ShortByteString.
--
-- Returns a ShortByteString of length n filled with
-- pseudo-random bytes.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> import Data.ByteString.Short (unpack)
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> unpack . fst $ uniformShortByteString 10 pureGen
-- [51,123,251,37,49,167,90,109,1,4]
--
uniformShortByteString :: RandomGen g => Int -> g -> (ShortByteString, g)
-- | Fill in a slice of a mutable byte array with randomly generated bytes.
-- This function does not fail, instead it clamps the offset and number
-- of bytes to generate into a valid range.
uniformFillMutableByteArray :: RandomGen g => MutableByteArray s -> Int -> Int -> g -> ST s g
-- | Generates a ByteString of the specified size using a pure
-- pseudo-random number generator. See uniformByteStringM for
-- the monadic version.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> import Data.ByteString
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> :seti -Wno-deprecations
--
-- >>> unpack . fst . genByteString 10 $ pureGen
-- [51,123,251,37,49,167,90,109,1,4]
--
-- | Deprecated: In favor of uniformByteString
genByteString :: RandomGen g => Int -> g -> (ByteString, g)
-- | Same as uniformByteArray False, but for
-- ShortByteString.
--
-- genShortByteString n g returns a ShortByteString of
-- length n filled with pseudo-random bytes.
--
-- Note - This function will be removed from the type class in the
-- next major release as it is no longer needed because of
-- unsafeUniformFillMutableByteArray.
-- | Deprecated: In favor of uniformShortByteString
genShortByteString :: RandomGen g => Int -> g -> (ShortByteString, g)
-- | The standard pseudo-random number generator.
data StdGen
-- | Constructs a StdGen deterministically from an Int seed.
-- See mkStdGen64 for a Word64 variant that is architecture
-- agnostic.
mkStdGen :: Int -> StdGen
-- | Constructs a StdGen deterministically from a Word64
-- seed.
--
-- The difference between mkStdGen is that mkStdGen64 will
-- work the same on 64-bit and 32-bit architectures, while the former can
-- only use 32-bit of information for initializing the psuedo-random
-- number generator on 32-bit operating systems
mkStdGen64 :: Word64 -> StdGen
-- | Initialize StdGen using system entropy (i.e.
-- /dev/urandom) when it is available, while falling back on
-- using system time as the seed.
initStdGen :: MonadIO m => m StdGen
-- | Uses the supplied function to get a value from the current global
-- random generator, and updates the global generator with the new
-- generator returned by the function. For example, rollDice
-- produces a pseudo-random integer between 1 and 6:
--
--
-- >>> rollDice = getStdRandom (randomR (1, 6))
--
-- >>> replicateM 10 (rollDice :: IO Int)
-- [1,1,1,4,5,6,1,2,2,5]
--
--
-- This is an outdated function and it is recommended to switch to its
-- equivalent applyAtomicGen instead, possibly with the
-- globalStdGen if relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> rollDice = applyAtomicGen (uniformR (1, 6)) globalStdGen
--
-- >>> replicateM 10 (rollDice :: IO Int)
-- [2,1,1,5,4,3,6,6,3,2]
--
getStdRandom :: MonadIO m => (StdGen -> (a, StdGen)) -> m a
-- | Gets the global pseudo-random number generator. Extracts the contents
-- of globalStdGen
getStdGen :: MonadIO m => m StdGen
-- | Sets the global pseudo-random number generator. Overwrites the
-- contents of globalStdGen
setStdGen :: MonadIO m => StdGen -> m ()
-- | Applies split to the current global pseudo-random generator
-- globalStdGen, updates it with one of the results, and returns
-- the other.
newStdGen :: MonadIO m => m StdGen
-- | A variant of randomM that uses the global pseudo-random number
-- generator globalStdGen.
--
--
-- >>> import Data.Int
--
-- >>> randomIO :: IO Int32
-- 114794456
--
--
-- This function is equivalent to getStdRandom
-- random and is included in this interface for historical
-- reasons and backwards compatibility. It is recommended to use
-- uniformM instead, possibly with the globalStdGen if
-- relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> uniformM globalStdGen :: IO Int32
-- -1768545016
--
randomIO :: (Random a, MonadIO m) => m a
-- | A variant of randomRM that uses the global pseudo-random number
-- generator globalStdGen
--
--
-- >>> randomRIO (2020, 2100) :: IO Int
-- 2028
--
--
-- Similar to randomIO, this function is equivalent to
-- getStdRandom randomR and is included in this
-- interface for historical reasons and backwards compatibility. It is
-- recommended to use uniformRM instead, possibly with the
-- globalStdGen if relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> uniformRM (2020, 2100) globalStdGen :: IO Int
-- 2044
--
randomRIO :: (Random a, MonadIO m) => (a, a) -> m a
-- | Yields the range of values returned by next.
--
-- It is required that:
--
--
-- - If (a, b) = genRange g, then a <
-- b.
-- - genRange must not examine its argument so the value it
-- returns is determined only by the instance of RandomGen.
--
--
-- The default definition spans the full range of Int.
-- | Deprecated: No longer used
genRange :: RandomGen g => g -> (Int, Int)
-- | Returns an Int that is uniformly distributed over the range
-- returned by genRange (including both end points), and a new
-- generator. Using next is inefficient as all operations go via
-- Integer. See here for more details. It is thus
-- deprecated.
-- | Deprecated: No longer used
next :: RandomGen g => g -> (Int, g)
instance System.Random.Random GHC.Num.Integer.Integer
instance System.Random.Random GHC.Int.Int8
instance System.Random.Random GHC.Int.Int16
instance System.Random.Random GHC.Int.Int32
instance System.Random.Random GHC.Int.Int64
instance System.Random.Random GHC.Types.Int
instance System.Random.Random GHC.Types.Word
instance System.Random.Random GHC.Word.Word8
instance System.Random.Random GHC.Word.Word16
instance System.Random.Random GHC.Word.Word32
instance System.Random.Random GHC.Word.Word64
instance System.Random.Random Foreign.C.Types.CBool
instance System.Random.Random Foreign.C.Types.CChar
instance System.Random.Random Foreign.C.Types.CSChar
instance System.Random.Random Foreign.C.Types.CUChar
instance System.Random.Random Foreign.C.Types.CShort
instance System.Random.Random Foreign.C.Types.CUShort
instance System.Random.Random Foreign.C.Types.CInt
instance System.Random.Random Foreign.C.Types.CUInt
instance System.Random.Random Foreign.C.Types.CLong
instance System.Random.Random Foreign.C.Types.CULong
instance System.Random.Random Foreign.C.Types.CPtrdiff
instance System.Random.Random Foreign.C.Types.CSize
instance System.Random.Random Foreign.C.Types.CWchar
instance System.Random.Random Foreign.C.Types.CSigAtomic
instance System.Random.Random Foreign.C.Types.CLLong
instance System.Random.Random Foreign.C.Types.CULLong
instance System.Random.Random Foreign.C.Types.CIntPtr
instance System.Random.Random Foreign.C.Types.CUIntPtr
instance System.Random.Random Foreign.C.Types.CIntMax
instance System.Random.Random Foreign.C.Types.CUIntMax
instance System.Random.Random Foreign.C.Types.CFloat
instance System.Random.Random Foreign.C.Types.CDouble
instance System.Random.Random GHC.Types.Char
instance System.Random.Random GHC.Types.Bool
instance System.Random.Random GHC.Types.Double
instance System.Random.Random GHC.Types.Float
instance (System.Random.Random a, System.Random.Random b) => System.Random.Random (a, b)
instance (System.Random.Random a, System.Random.Random b, System.Random.Random c) => System.Random.Random (a, b, c)
instance (System.Random.Random a, System.Random.Random b, System.Random.Random c, System.Random.Random d) => System.Random.Random (a, b, c, d)
instance (System.Random.Random a, System.Random.Random b, System.Random.Random c, System.Random.Random d, System.Random.Random e) => System.Random.Random (a, b, c, d, e)
instance (System.Random.Random a, System.Random.Random b, System.Random.Random c, System.Random.Random d, System.Random.Random e, System.Random.Random f) => System.Random.Random (a, b, c, d, e, f)
instance (System.Random.Random a, System.Random.Random b, System.Random.Random c, System.Random.Random d, System.Random.Random e, System.Random.Random f, System.Random.Random g) => System.Random.Random (a, b, c, d, e, f, g)
-- | This library deals with the common task of pseudo-random number
-- generation.
module System.Random.Stateful
-- | RandomGen is an interface to pure pseudo-random number
-- generators.
--
-- StdGen is the standard RandomGen instance provided by
-- this library.
class RandomGen g
-- | Returns an Int that is uniformly distributed over the range
-- returned by genRange (including both end points), and a new
-- generator. Using next is inefficient as all operations go via
-- Integer. See here for more details. It is thus
-- deprecated.
-- | Deprecated: No longer used
next :: RandomGen g => g -> (Int, g)
-- | Returns a Word8 that is uniformly distributed over the entire
-- Word8 range.
genWord8 :: RandomGen g => g -> (Word8, g)
-- | Returns a Word16 that is uniformly distributed over the entire
-- Word16 range.
genWord16 :: RandomGen g => g -> (Word16, g)
-- | Returns a Word32 that is uniformly distributed over the entire
-- Word32 range.
genWord32 :: RandomGen g => g -> (Word32, g)
-- | Returns a Word64 that is uniformly distributed over the entire
-- Word64 range.
genWord64 :: RandomGen g => g -> (Word64, g)
-- | genWord32R upperBound g returns a Word32 that is
-- uniformly distributed over the range [0, upperBound].
genWord32R :: RandomGen g => Word32 -> g -> (Word32, g)
-- | genWord64R upperBound g returns a Word64 that is
-- uniformly distributed over the range [0, upperBound].
genWord64R :: RandomGen g => Word64 -> g -> (Word64, g)
-- | Same as uniformByteArray False, but for
-- ShortByteString.
--
-- genShortByteString n g returns a ShortByteString of
-- length n filled with pseudo-random bytes.
--
-- Note - This function will be removed from the type class in the
-- next major release as it is no longer needed because of
-- unsafeUniformFillMutableByteArray.
-- | Deprecated: In favor of uniformShortByteString
genShortByteString :: RandomGen g => Int -> g -> (ShortByteString, g)
-- | Fill in the supplied MutableByteArray with uniformly generated
-- random bytes. This function is unsafe because it is not required to do
-- any bounds checking. For a safe variant use
-- uniformFillMutableByteArrayM instead.
--
-- Default type class implementation uses
-- defaultUnsafeUniformFillMutableByteArray.
unsafeUniformFillMutableByteArray :: RandomGen g => MutableByteArray s -> Int -> Int -> g -> ST s g
-- | Yields the range of values returned by next.
--
-- It is required that:
--
--
-- - If (a, b) = genRange g, then a <
-- b.
-- - genRange must not examine its argument so the value it
-- returns is determined only by the instance of RandomGen.
--
--
-- The default definition spans the full range of Int.
-- | Deprecated: No longer used
genRange :: RandomGen g => g -> (Int, Int)
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated. Some pseudo-random number generators
-- are not splittable. In that case, the split implementation
-- should fail with a descriptive error message.
-- | Deprecated: In favor of splitGen
split :: RandomGen g => g -> (g, g)
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated. Some pseudo-random number generators
-- are not splittable. In that case, the split implementation
-- should fail with a descriptive error message.
-- | Deprecated: In favor of splitGen
split :: (RandomGen g, SplitGen g) => g -> (g, g)
-- | Pseudo-random generators that can be split into two separate and
-- independent psuedo-random generators should provide an instance for
-- this type class.
--
-- Historically this functionality was included in the RandomGen
-- type class in the split function, however, few pseudo-random
-- generators possess this property of splittability. This lead the old
-- split function being usually implemented in terms of
-- error.
class RandomGen g => SplitGen g
-- | Returns two distinct pseudo-random number generators.
--
-- Implementations should take care to ensure that the resulting
-- generators are not correlated.
splitGen :: SplitGen g => g -> (g, g)
-- | The class of types for which random values can be generated. Most
-- instances of Random will produce values that are uniformly
-- distributed on the full range, but for those types without a
-- well-defined "full range" some sensible default subrange will be
-- selected.
--
-- Random exists primarily for backwards compatibility with
-- version 1.1 of this library. In new code, use the better specified
-- Uniform and UniformRange instead.
class Random a
-- | Takes a range (lo,hi) and a pseudo-random number generator
-- g, and returns a pseudo-random value uniformly distributed over
-- the closed interval [lo,hi], together with a new generator. It
-- is unspecified what happens if lo>hi, but usually the values
-- will simply get swapped.
--
--
-- >>> let gen = mkStdGen 26
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
--
-- For continuous types there is no requirement that the values lo
-- and hi are ever produced, but they may be, depending on the
-- implementation and the interval.
--
-- There is no requirement to follow the Ord instance and the
-- concept of range can be defined on per type basis. For example product
-- types will treat their values independently:
--
--
-- >>> fst $ randomR (('a', 5.0), ('z', 10.0)) $ mkStdGen 26
-- ('z',5.22694980853051)
--
--
-- In case when a lawful range is desired uniformR should be used
-- instead.
randomR :: (Random a, RandomGen g) => (a, a) -> g -> (a, g)
-- | Takes a range (lo,hi) and a pseudo-random number generator
-- g, and returns a pseudo-random value uniformly distributed over
-- the closed interval [lo,hi], together with a new generator. It
-- is unspecified what happens if lo>hi, but usually the values
-- will simply get swapped.
--
--
-- >>> let gen = mkStdGen 26
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
-- >>> fst $ randomR ('a', 'z') gen
-- 'z'
--
--
-- For continuous types there is no requirement that the values lo
-- and hi are ever produced, but they may be, depending on the
-- implementation and the interval.
--
-- There is no requirement to follow the Ord instance and the
-- concept of range can be defined on per type basis. For example product
-- types will treat their values independently:
--
--
-- >>> fst $ randomR (('a', 5.0), ('z', 10.0)) $ mkStdGen 26
-- ('z',5.22694980853051)
--
--
-- In case when a lawful range is desired uniformR should be used
-- instead.
randomR :: (Random a, RandomGen g, UniformRange a) => (a, a) -> g -> (a, g)
-- | The same as randomR, but using a default range determined by
-- the type:
--
--
-- - For bounded types (instances of Bounded, such as
-- Char), the range is normally the whole type.
-- - For floating point types, the range is normally the closed
-- interval [0,1].
-- - For Integer, the range is (arbitrarily) the range of
-- Int.
--
random :: (Random a, RandomGen g) => g -> (a, g)
-- | The same as randomR, but using a default range determined by
-- the type:
--
--
-- - For bounded types (instances of Bounded, such as
-- Char), the range is normally the whole type.
-- - For floating point types, the range is normally the closed
-- interval [0,1].
-- - For Integer, the range is (arbitrarily) the range of
-- Int.
--
random :: (Random a, RandomGen g, Uniform a) => g -> (a, g)
-- | Plural variant of randomR, producing an infinite list of
-- pseudo-random values instead of returning a new generator.
randomRs :: (Random a, RandomGen g) => (a, a) -> g -> [a]
-- | Plural variant of random, producing an infinite list of
-- pseudo-random values instead of returning a new generator.
randoms :: (Random a, RandomGen g) => g -> [a]
-- | The class of types for which a uniformly distributed value can be
-- drawn from all possible values of the type.
class Uniform a
-- | The class of types for which a uniformly distributed value can be
-- drawn from a range.
class UniformRange a
-- | A type class for data with a finite number of inhabitants. This type
-- class is used in the default implementation of Uniform.
--
-- Users are not supposed to write instances of Finite manually.
-- There is a default implementation in terms of Generic instead.
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> data MyBool = MyTrue | MyFalse deriving (Generic, Finite)
--
-- >>> data Action = Code MyBool | Eat (Maybe Bool) | Sleep deriving (Generic, Finite)
--
class Finite a
-- | This is a binary form of pseudo-random number generator's state. It is
-- designed to be safe and easy to use for input/output operations like
-- restoring from file, transmitting over the network, etc.
--
-- Constructor is not exported, becasue it is important for
-- implementation to enforce the invariant of the underlying byte array
-- being of the exact same length as the generator has specified in
-- SeedSize. Use mkSize and unSize to get access to
-- the raw bytes in a safe manner.
data Seed g
-- | The standard pseudo-random number generator.
data StdGen
-- | Interface for converting a pure pseudo-random number generator to and
-- from non-empty sequence of bytes. Seeds are stored in Little-Endian
-- order regardless of the platform it is being used on, which provides
-- cross-platform compatibility, while providing optimal performance for
-- the most common platform type.
--
-- Conversion to and from a Seed serves as a building block for
-- implementing serialization for any pure or frozen pseudo-random number
-- generator.
--
-- It is not trivial to implement platform independence. For this reason
-- this type class has two alternative ways of creating an instance for
-- this class. The easiest way for constructing a platform indepent seed
-- is by converting the inner state of a generator to and from a list of
-- 64 bit words using toSeed64 and fromSeed64 respectively.
-- In that case cross-platform support will be handled automaticaly.
--
--
-- >>> :set -XDataKinds -XTypeFamilies
--
-- >>> import Data.Word (Word8, Word32)
--
-- >>> import Data.Bits ((.|.), shiftR, shiftL)
--
-- >>> import Data.List.NonEmpty (NonEmpty ((:|)))
--
-- >>> data FiveByteGen = FiveByteGen Word8 Word32 deriving Show
--
-- >>> :{
-- instance SeedGen FiveByteGen where
-- type SeedSize FiveByteGen = 5
-- fromSeed64 (w64 :| _) =
-- FiveByteGen (fromIntegral (w64 `shiftR` 32)) (fromIntegral w64)
-- toSeed64 (FiveByteGen x1 x4) =
-- let w64 = (fromIntegral x1 `shiftL` 32) .|. fromIntegral x4
-- in (w64 :| [])
-- :}
--
--
--
-- >>> FiveByteGen 0x80 0x01020304
-- FiveByteGen 128 16909060
--
-- >>> fromSeed (toSeed (FiveByteGen 0x80 0x01020304))
-- FiveByteGen 128 16909060
--
-- >>> toSeed (FiveByteGen 0x80 0x01020304)
-- Seed [0x04, 0x03, 0x02, 0x01, 0x80]
--
-- >>> toSeed64 (FiveByteGen 0x80 0x01020304)
-- 549772722948 :| []
--
--
-- However, when performance is of utmost importance or default handling
-- of cross platform independence is not sufficient, then an adventurous
-- developer can try implementing conversion into bytes directly with
-- toSeed and fromSeed.
--
-- Properties that must hold:
--
--
-- > fromSeed (toSeed gen) == gen
--
--
--
-- > fromSeed64 (toSeed64 gen) == gen
--
--
-- Note, that there is no requirement for every Seed to roundtrip,
-- eg. this proprty does not even hold for StdGen:
--
--
-- >>> let seed = nonEmptyToSeed (0xab :| [0xff00]) :: Seed StdGen
--
-- >>> seed == toSeed (fromSeed seed)
-- False
--
class (KnownNat (SeedSize g), 1 <= SeedSize g, Typeable g) => SeedGen g where {
-- | Number of bytes that is required for storing the full state of a
-- pseudo-random number generator. It should be big enough to satisfy the
-- roundtrip property:
--
--
-- > fromSeed (toSeed gen) == gen
--
type SeedSize g :: Nat;
}
-- | Convert from a binary representation to a pseudo-random number
-- generator
fromSeed :: SeedGen g => Seed g -> g
-- | Convert to a binary representation of a pseudo-random number generator
toSeed :: SeedGen g => g -> Seed g
-- | Construct pseudo-random number generator from a list of words.
-- Whenever list does not have enough bytes to satisfy the
-- SeedSize requirement, it will be padded with zeros. On the
-- other hand when it has more than necessary, extra bytes will be
-- dropped.
--
-- For example if SeedSize is set to 2, then only the lower 16
-- bits of the first element in the list will be used.
fromSeed64 :: SeedGen g => NonEmpty Word64 -> g
-- | Convert pseudo-random number generator to a list of words
--
-- In case when SeedSize is not a multiple of 8, then the upper
-- bits of the last word in the list will be set to zero.
toSeed64 :: SeedGen g => g -> NonEmpty Word64
-- | Generates a value uniformly distributed over all possible values of
-- that type.
--
-- This is a pure version of uniformM.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> uniform pureGen :: (Bool, StdGen)
-- (True,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> uniform @Bool pureGen
-- (True,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
uniform :: (Uniform a, RandomGen g) => g -> (a, g)
-- | Generates a value uniformly distributed over the provided range, which
-- is interpreted as inclusive in the lower and upper bound.
--
--
-- - uniformR (1 :: Int, 4 :: Int) generates values uniformly
-- from the set <math>
-- - uniformR (1 :: Float, 4 :: Float) generates values
-- uniformly from the set <math>
--
--
-- The following law should hold to make the function always defined:
--
--
-- uniformR (a, b) = uniformR (b, a)
--
--
-- This is a pure version of uniformRM.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> uniformR (1 :: Int, 4 :: Int) pureGen
-- (4,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> uniformR @Int (1, 4) pureGen
-- (4,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
uniformR :: (UniformRange a, RandomGen g) => (a, a) -> g -> (a, g)
-- | Produce an infinite list of pseudo-random values. Integrates nicely
-- with list fusion. Naturally, there is no way to recover the final
-- generator, therefore either use split before calling
-- uniforms or use uniformList instead.
--
-- Similar to randoms, except it relies on Uniform type
-- class instead of Random
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> import Data.Word (Word16)
--
-- >>> take 5 $ uniforms gen :: [Word16]
-- [56342,15850,25292,14347,13919]
--
uniforms :: (Uniform a, RandomGen g) => g -> [a]
-- | Produce an infinite list of pseudo-random values in a specified range.
-- Same as uniforms, integrates nicely with list fusion. There is
-- no way to recover the final generator, therefore either use
-- split before calling uniformRs or use
-- uniformListR instead.
--
-- Similar to randomRs, except it relies on UniformRange
-- type class instead of Random.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> take 5 $ uniformRs (10, 100) gen :: [Int]
-- [32,86,21,57,39]
--
uniformRs :: (UniformRange a, RandomGen g) => (a, a) -> g -> [a]
-- | Produce a list of the supplied length with elements generated
-- uniformly.
--
-- See uniformListM for a stateful counterpart.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> import Data.Word (Word16)
--
-- >>> uniformList 5 gen :: ([Word16], StdGen)
-- ([56342,15850,25292,14347,13919],StdGen {unStdGen = SMGen 6446154349414395371 1920468677557965761})
--
uniformList :: (Uniform a, RandomGen g) => Int -> g -> ([a], g)
-- | Produce a list of the supplied length with elements generated
-- uniformly.
--
-- See uniformListM for a stateful counterpart.
--
-- Examples
--
--
-- >>> let gen = mkStdGen 2023
--
-- >>> uniformListR 10 (20, 30) gen :: ([Int], StdGen)
-- ([26,30,27,24,30,25,27,21,27,27],StdGen {unStdGen = SMGen 12965503083958398648 1920468677557965761})
--
uniformListR :: (UniformRange a, RandomGen g) => Int -> (a, a) -> g -> ([a], g)
-- | Shuffle elements of a list in a uniformly random order.
--
-- Examples
--
--
-- >>> uniformShuffleList "ELVIS" $ mkStdGen 252
-- ("LIVES",StdGen {unStdGen = SMGen 17676540583805057877 5302934877338729551})
--
uniformShuffleList :: RandomGen g => [a] -> g -> ([a], g)
-- | Efficiently generates a sequence of pseudo-random bytes in a platform
-- independent manner.
uniformByteArray :: RandomGen g => Bool -> Int -> g -> (ByteArray, g)
-- | Generates a ByteString of the specified size using a pure
-- pseudo-random number generator. See uniformByteStringM for
-- the monadic version.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> import Data.ByteString (unpack)
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> unpack . fst $ uniformByteString 10 pureGen
-- [51,123,251,37,49,167,90,109,1,4]
--
uniformByteString :: RandomGen g => Int -> g -> (ByteString, g)
-- | Fill in a slice of a mutable byte array with randomly generated bytes.
-- This function does not fail, instead it clamps the offset and number
-- of bytes to generate into a valid range.
uniformFillMutableByteArray :: RandomGen g => MutableByteArray s -> Int -> Int -> g -> ST s g
-- | Generates a ByteString of the specified size using a pure
-- pseudo-random number generator. See uniformByteStringM for
-- the monadic version.
--
-- Examples
--
--
-- >>> import System.Random
--
-- >>> import Data.ByteString
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> :seti -Wno-deprecations
--
-- >>> unpack . fst . genByteString 10 $ pureGen
-- [51,123,251,37,49,167,90,109,1,4]
--
-- | Deprecated: In favor of uniformByteString
genByteString :: RandomGen g => Int -> g -> (ByteString, g)
-- | Constructs a StdGen deterministically from an Int seed.
-- See mkStdGen64 for a Word64 variant that is architecture
-- agnostic.
mkStdGen :: Int -> StdGen
-- | Constructs a StdGen deterministically from a Word64
-- seed.
--
-- The difference between mkStdGen is that mkStdGen64 will
-- work the same on 64-bit and 32-bit architectures, while the former can
-- only use 32-bit of information for initializing the psuedo-random
-- number generator on 32-bit operating systems
mkStdGen64 :: Word64 -> StdGen
-- | Initialize StdGen using system entropy (i.e.
-- /dev/urandom) when it is available, while falling back on
-- using system time as the seed.
initStdGen :: MonadIO m => m StdGen
-- | Uses the supplied function to get a value from the current global
-- random generator, and updates the global generator with the new
-- generator returned by the function. For example, rollDice
-- produces a pseudo-random integer between 1 and 6:
--
--
-- >>> rollDice = getStdRandom (randomR (1, 6))
--
-- >>> replicateM 10 (rollDice :: IO Int)
-- [1,1,1,4,5,6,1,2,2,5]
--
--
-- This is an outdated function and it is recommended to switch to its
-- equivalent applyAtomicGen instead, possibly with the
-- globalStdGen if relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> rollDice = applyAtomicGen (uniformR (1, 6)) globalStdGen
--
-- >>> replicateM 10 (rollDice :: IO Int)
-- [2,1,1,5,4,3,6,6,3,2]
--
getStdRandom :: MonadIO m => (StdGen -> (a, StdGen)) -> m a
-- | Gets the global pseudo-random number generator. Extracts the contents
-- of globalStdGen
getStdGen :: MonadIO m => m StdGen
-- | Sets the global pseudo-random number generator. Overwrites the
-- contents of globalStdGen
setStdGen :: MonadIO m => StdGen -> m ()
-- | Applies split to the current global pseudo-random generator
-- globalStdGen, updates it with one of the results, and returns
-- the other.
newStdGen :: MonadIO m => m StdGen
-- | A variant of randomM that uses the global pseudo-random number
-- generator globalStdGen.
--
--
-- >>> import Data.Int
--
-- >>> randomIO :: IO Int32
-- 114794456
--
--
-- This function is equivalent to getStdRandom
-- random and is included in this interface for historical
-- reasons and backwards compatibility. It is recommended to use
-- uniformM instead, possibly with the globalStdGen if
-- relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> uniformM globalStdGen :: IO Int32
-- -1768545016
--
randomIO :: (Random a, MonadIO m) => m a
-- | A variant of randomRM that uses the global pseudo-random number
-- generator globalStdGen
--
--
-- >>> randomRIO (2020, 2100) :: IO Int
-- 2028
--
--
-- Similar to randomIO, this function is equivalent to
-- getStdRandom randomR and is included in this
-- interface for historical reasons and backwards compatibility. It is
-- recommended to use uniformRM instead, possibly with the
-- globalStdGen if relying on the global state is acceptable.
--
--
-- >>> import System.Random.Stateful
--
-- >>> uniformRM (2020, 2100) globalStdGen :: IO Int
-- 2044
--
randomRIO :: (Random a, MonadIO m) => (a, a) -> m a
-- | Get the expected size of the Seed in number bytes
seedSize :: forall g. SeedGen g => Int
-- | Just like seedSize, except it accepts a proxy as an argument.
seedSizeProxy :: forall proxy g. SeedGen g => proxy g -> Int
-- | Construct a Seed from a ByteArray of expected length.
-- Whenever ByteArray does not match the SeedSize specified
-- by the pseudo-random generator, this function will fail.
mkSeed :: forall g m. (SeedGen g, MonadFail m) => ByteArray -> m (Seed g)
-- | Unwrap the Seed and get the underlying ByteArray
unSeed :: Seed g -> ByteArray
-- | Just like mkSeed, but uses ByteString as argument.
-- Results in a memcopy of the seed.
mkSeedFromByteString :: (SeedGen g, MonadFail m) => ByteString -> m (Seed g)
-- | Just like unSeed, but produced a ByteString. Results
-- in a memcopy of the seed.
unSeedToByteString :: Seed g -> ByteString
-- | Helper function that allows for operating directly on the Seed,
-- while supplying a function that uses the pseudo-random number
-- generator that is constructed from that Seed.
--
-- Example
--
--
-- >>> :set -XTypeApplications
--
-- >>> import System.Random
--
-- >>> withSeed (nonEmptyToSeed (pure 2024) :: Seed StdGen) (uniform @Int)
-- (1039666877624726199,Seed [0xe9, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00])
--
withSeed :: SeedGen g => Seed g -> (g -> (a, g)) -> (a, Seed g)
-- | Same as withSeed, except it is useful with monadic computation
-- and frozen generators.
--
-- See withSeedMutableGen for a helper that also handles seeds for
-- mutable pseduo-random number generators.
withSeedM :: (SeedGen g, Functor f) => Seed g -> (g -> f (a, g)) -> f (a, Seed g)
-- | Read the seed from a file and use it for constructing a pseudo-random
-- number generator. After supplied action has been applied to the
-- constructed generator, the resulting generator will be converted back
-- to a seed and written to the same file.
withSeedFile :: (SeedGen g, MonadIO m) => FilePath -> (Seed g -> m (a, Seed g)) -> m a
-- | This is a function that shows the name of the generator type, which is
-- useful for error reporting.
seedGenTypeName :: forall g. SeedGen g => String
-- | Construct a seed from a list of 64-bit words. At most SeedSize
-- many bytes will be used.
nonEmptyToSeed :: forall g. SeedGen g => NonEmpty Word64 -> Seed g
-- | Convert a Seed to a list of 64bit words.
nonEmptyFromSeed :: forall g. SeedGen g => Seed g -> NonEmpty Word64
-- | StatefulGen is an interface to monadic pseudo-random number
-- generators.
class Monad m => StatefulGen g m
-- | uniformWord32R upperBound g generates a Word32 that is
-- uniformly distributed over the range [0, upperBound].
uniformWord32R :: StatefulGen g m => Word32 -> g -> m Word32
-- | uniformWord64R upperBound g generates a Word64 that is
-- uniformly distributed over the range [0, upperBound].
uniformWord64R :: StatefulGen g m => Word64 -> g -> m Word64
-- | Generates a Word8 that is uniformly distributed over the entire
-- Word8 range.
--
-- The default implementation extracts a Word8 from
-- uniformWord32.
uniformWord8 :: StatefulGen g m => g -> m Word8
-- | Generates a Word16 that is uniformly distributed over the
-- entire Word16 range.
--
-- The default implementation extracts a Word16 from
-- uniformWord32.
uniformWord16 :: StatefulGen g m => g -> m Word16
-- | Generates a Word32 that is uniformly distributed over the
-- entire Word32 range.
--
-- The default implementation extracts a Word32 from
-- uniformWord64.
uniformWord32 :: StatefulGen g m => g -> m Word32
-- | Generates a Word64 that is uniformly distributed over the
-- entire Word64 range.
--
-- The default implementation combines two Word32 from
-- uniformWord32 into one Word64.
uniformWord64 :: StatefulGen g m => g -> m Word64
-- | uniformShortByteString n g generates a ShortByteString
-- of length n filled with pseudo-random bytes.
-- | Deprecated: In favor of uniformShortByteStringM
uniformShortByteString :: StatefulGen g m => Int -> g -> m ShortByteString
-- | This class is designed for mutable pseudo-random number generators
-- that have a frozen imutable counterpart that can be manipulated in
-- pure code.
--
-- It also works great with frozen generators that are based on pure
-- generators that have a RandomGen instance.
--
-- Here are a few laws, which are important for this type class:
--
--
-- - Roundtrip and complete destruction on overwrite:
--
--
--
-- overwriteGen mg fg >> freezeGen mg = pure fg
--
--
--
-- - Modification of a mutable generator:
--
--
--
-- overwriteGen mg fg = modifyGen mg (const ((), fg)
--
--
--
-- - Freezing of a mutable generator:
--
--
--
-- freezeGen mg = modifyGen mg (fg -> (fg, fg))
--
class StatefulGen (MutableGen f m) m => FrozenGen f m where {
-- | Represents the state of the pseudo-random number generator for use
-- with thawGen and freezeGen.
type MutableGen f m = (g :: Type) | g -> f;
}
-- | Saves the state of the pseudo-random number generator as a frozen
-- seed.
freezeGen :: FrozenGen f m => MutableGen f m -> m f
-- | Apply a pure function to the frozen pseudo-random number generator.
modifyGen :: FrozenGen f m => MutableGen f m -> (f -> (a, f)) -> m a
-- | Overwrite contents of the mutable pseudo-random number generator with
-- the supplied frozen one
overwriteGen :: FrozenGen f m => MutableGen f m -> f -> m ()
-- | Functionality for thawing frozen generators is not part of the
-- FrozenGen class, becase not all mutable generators support
-- functionality of creating new mutable generators, which is what
-- thawing is in its essence. For this reason StateGen does not
-- have an instance for this type class, but it has one for
-- FrozenGen.
--
-- Here is an important law that relates this type class to
-- FrozenGen
--
--
-- - Roundtrip and independence of mutable generators:
--
--
--
-- traverse thawGen fgs >>= traverse freezeGen = pure fgs
--
class FrozenGen f m => ThawedGen f m
-- | Create a new mutable pseudo-random number generator from its frozen
-- state.
thawGen :: ThawedGen f m => f -> m (MutableGen f m)
-- | Runs a mutable pseudo-random number generator from its
-- FrozenGen state.
--
-- Examples
--
--
-- >>> import Data.Int (Int8)
--
-- >>> withMutableGen (IOGen (mkStdGen 217)) (uniformListM 5) :: IO ([Int8], IOGen StdGen)
-- ([-74,37,-50,-2,3],IOGen {unIOGen = StdGen {unStdGen = SMGen 4273268533320920145 15251669095119325999}})
--
withMutableGen :: ThawedGen f m => f -> (MutableGen f m -> m a) -> m (a, f)
-- | Same as withMutableGen, but only returns the generated value.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> withMutableGen_ (IOGen pureGen) (uniformRM (1 :: Int, 6 :: Int))
-- 4
--
withMutableGen_ :: ThawedGen f m => f -> (MutableGen f m -> m a) -> m a
-- | Just like withMutableGen, except uses a Seed instead of
-- a frozen generator.
--
-- Examples
--
-- Here is good example of how withSeedMutableGen can be used with
-- withSeedFile, which uses a locally stored seed.
--
-- First we define a reportSeed function that will print the
-- contents of a seed file as a list of bytes:
--
--
-- >>> import Data.ByteString as BS (readFile, writeFile, unpack)
--
-- >>> :seti -XOverloadedStrings
--
-- >>> let reportSeed fp = print . ("Seed: " <>) . show . BS.unpack =<< BS.readFile fp
--
--
-- Given a file path, write an StdGen seed into the file:
--
--
-- >>> :seti -XFlexibleContexts -XScopedTypeVariables
--
-- >>> let writeInitSeed fp = BS.writeFile fp (unSeedToByteString (toSeed (mkStdGen 2025)))
--
--
-- Apply a StatefulGen monadic action that uses IOGen
-- StdGen, restored from the seed in the given path:
--
--
-- >>> let withMutableSeedFile fp action = withSeedFile fp (\(seed :: Seed (IOGen StdGen)) -> withSeedMutableGen seed action)
--
--
-- Given a path and an action initialize the seed file and apply the
-- action using that seed:
--
--
-- >>> let withInitSeedFile fp action = writeInitSeed fp *> reportSeed fp *> withMutableSeedFile fp action <* reportSeed fp
--
--
-- For the sake of example we will use a temporary directory for storing
-- the seed. Here we report the contents of the seed file before and
-- after we shuffle a list:
--
--
-- >>> import UnliftIO.Temporary (withSystemTempDirectory)
--
-- >>> withSystemTempDirectory "random" (\fp -> withInitSeedFile (fp ++ "/seed.bin") (uniformShuffleListM [1..10]))
-- "Seed: [183,178,143,77,132,163,109,14,157,105,82,99,148,82,109,173]"
-- "Seed: [60,105,117,203,187,138,69,39,157,105,82,99,148,82,109,173]"
-- [7,5,4,3,1,8,10,6,9,2]
--
withSeedMutableGen :: (SeedGen g, ThawedGen g m) => Seed g -> (MutableGen g m -> m a) -> m (a, Seed g)
-- | Just like withSeedMutableGen, except it doesn't return the
-- final generator, only the resulting value. This is slightly more
-- efficient, since it doesn't incur overhead from freezeing the mutable
-- generator
withSeedMutableGen_ :: (SeedGen g, ThawedGen g m) => Seed g -> (MutableGen g m -> m a) -> m a
-- | Generates a pseudo-random value using monadic interface and
-- Random instance.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 139
--
-- >>> g <- newIOGenM pureGen
--
-- >>> randomM g :: IO Double
-- 0.33775117339631733
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> randomM @Double g
-- 0.9156875994165681
--
randomM :: forall a g m. (Random a, RandomGen g, FrozenGen g m) => MutableGen g m -> m a
-- | Generates a pseudo-random value using monadic interface and
-- Random instance.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> g <- newIOGenM pureGen
--
-- >>> randomRM (1, 100) g :: IO Int
-- 52
--
--
-- You can use type applications to disambiguate the type of the
-- generated numbers:
--
--
-- >>> :seti -XTypeApplications
--
-- >>> randomRM @Int (1, 100) g
-- 2
--
randomRM :: forall a g m. (Random a, RandomGen g, FrozenGen g m) => (a, a) -> MutableGen g m -> m a
-- | Splits a pseudo-random number generator into two. Overwrites the
-- mutable pseudo-random number generator with one of the immutable
-- pseudo-random number generators produced by a split function
-- and returns the other.
splitGenM :: (SplitGen f, FrozenGen f m) => MutableGen f m -> m f
-- | Splits a pseudo-random number generator into two. Overwrites the
-- mutable wrapper with one of the resulting generators and returns the
-- other as a new mutable generator.
splitMutableGenM :: (SplitGen f, ThawedGen f m) => MutableGen f m -> m (MutableGen f m)
-- | Interface to operations on RandomGen wrappers like
-- IOGenM and StateGenM.
-- | Deprecated: In favor of FrozenGen
class (RandomGen r, StatefulGen g m) => RandomGenM g r m | g -> r
-- | Deprecated: In favor of modifyGen
applyRandomGenM :: RandomGenM g r m => (r -> (a, r)) -> g -> m a
-- | Wrapper for pure state gen, which acts as an immutable seed for the
-- corresponding stateful generator StateGenM
newtype StateGen g
StateGen :: g -> StateGen g
[unStateGen] :: StateGen g -> g
-- | Opaque data type that carries the type of a pure pseudo-random number
-- generator.
data StateGenM g
StateGenM :: StateGenM g
-- | Runs a monadic generating action in the State monad using a
-- pure pseudo-random number generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> runStateGen pureGen randomM :: (Int, StdGen)
-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
runStateGen :: RandomGen g => g -> (StateGenM g -> State g a) -> (a, g)
-- | Runs a monadic generating action in the State monad using a
-- pure pseudo-random number generator. Returns only the resulting
-- pseudo-random value.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> runStateGen_ pureGen randomM :: Int
-- 7879794327570578227
--
runStateGen_ :: RandomGen g => g -> (StateGenM g -> State g a) -> a
-- | Runs a monadic generating action in the StateT monad using a
-- pure pseudo-random number generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> runStateGenT pureGen randomM :: IO (Int, StdGen)
-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
runStateGenT :: RandomGen g => g -> (StateGenM g -> StateT g m a) -> m (a, g)
-- | Runs a monadic generating action in the StateT monad using a
-- pure pseudo-random number generator. Returns only the resulting
-- pseudo-random value.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> runStateGenT_ pureGen randomM :: IO Int
-- 7879794327570578227
--
runStateGenT_ :: (RandomGen g, Functor f) => g -> (StateGenM g -> StateT g f a) -> f a
-- | Runs a monadic generating action in the ST monad using a pure
-- pseudo-random number generator.
runStateGenST :: RandomGen g => g -> (forall s. StateGenM g -> StateT g (ST s) a) -> (a, g)
-- | Runs a monadic generating action in the ST monad using a pure
-- pseudo-random number generator. Same as runStateGenST, but
-- discards the resulting generator.
runStateGenST_ :: RandomGen g => g -> (forall s. StateGenM g -> StateT g (ST s) a) -> a
-- | Frozen version of mutable AtomicGenM generator
newtype AtomicGen g
AtomicGen :: g -> AtomicGen g
[unAtomicGen] :: AtomicGen g -> g
-- | Wraps an IORef that holds a pure pseudo-random number
-- generator. All operations are performed atomically.
--
--
-- - AtomicGenM is safe in the presence of exceptions and
-- concurrency.
-- - AtomicGenM is the slowest of the monadic adapters due to
-- the overhead of its atomic operations.
--
newtype AtomicGenM g
AtomicGenM :: IORef g -> AtomicGenM g
[unAtomicGenM] :: AtomicGenM g -> IORef g
-- | Creates a new AtomicGenM.
newAtomicGenM :: MonadIO m => g -> m (AtomicGenM g)
-- | Atomically applies a pure operation to the wrapped pseudo-random
-- number generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> g <- newAtomicGenM pureGen
--
-- >>> applyAtomicGen random g :: IO Int
-- 7879794327570578227
--
applyAtomicGen :: MonadIO m => (g -> (a, g)) -> AtomicGenM g -> m a
-- | Global mutable standard pseudo-random number generator. This is the
-- same generator that was historically used by randomIO and
-- randomRIO functions.
--
--
-- >>> import Control.Monad (replicateM)
--
-- >>> replicateM 10 (uniformRM ('a', 'z') globalStdGen)
-- "tdzxhyfvgr"
--
globalStdGen :: AtomicGenM StdGen
-- | Frozen version of mutable IOGenM generator
newtype IOGen g
IOGen :: g -> IOGen g
[unIOGen] :: IOGen g -> g
-- | Wraps an IORef that holds a pure pseudo-random number
-- generator.
--
--
-- - IOGenM is safe in the presence of exceptions, but not
-- concurrency.
-- - IOGenM is slower than StateGenM due to the extra
-- pointer indirection.
-- - IOGenM is faster than AtomicGenM since the
-- IORef operations used by IOGenM are not atomic.
--
--
-- An example use case is writing pseudo-random bytes into a file:
--
--
-- >>> import UnliftIO.Temporary (withSystemTempFile)
--
-- >>> import Data.ByteString (hPutStr)
--
-- >>> let ioGen g = withSystemTempFile "foo.bin" $ \_ h -> uniformRM (0, 100) g >>= flip uniformByteStringM g >>= hPutStr h
--
--
-- and then run it:
--
--
-- >>> newIOGenM (mkStdGen 1729) >>= ioGen
--
newtype IOGenM g
IOGenM :: IORef g -> IOGenM g
[unIOGenM] :: IOGenM g -> IORef g
-- | Creates a new IOGenM.
newIOGenM :: MonadIO m => g -> m (IOGenM g)
-- | Applies a pure operation to the wrapped pseudo-random number
-- generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> g <- newIOGenM pureGen
--
-- >>> applyIOGen random g :: IO Int
-- 7879794327570578227
--
applyIOGen :: MonadIO m => (g -> (a, g)) -> IOGenM g -> m a
-- | Frozen version of mutable STGenM generator
newtype STGen g
STGen :: g -> STGen g
[unSTGen] :: STGen g -> g
-- | Wraps an STRef that holds a pure pseudo-random number
-- generator.
--
--
-- - STGenM is safe in the presence of exceptions, but not
-- concurrency.
-- - STGenM is slower than StateGenM due to the extra
-- pointer indirection.
--
newtype STGenM g s
STGenM :: STRef s g -> STGenM g s
[unSTGenM] :: STGenM g s -> STRef s g
-- | Creates a new STGenM.
newSTGenM :: g -> ST s (STGenM g s)
-- | Applies a pure operation to the wrapped pseudo-random number
-- generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> (runSTGen pureGen (\g -> applySTGen random g)) :: (Int, StdGen)
-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
applySTGen :: (g -> (a, g)) -> STGenM g s -> ST s a
-- | Runs a monadic generating action in the ST monad using a pure
-- pseudo-random number generator.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> (runSTGen pureGen (\g -> applySTGen random g)) :: (Int, StdGen)
-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})
--
runSTGen :: RandomGen g => g -> (forall s. STGenM g s -> ST s a) -> (a, g)
-- | Runs a monadic generating action in the ST monad using a pure
-- pseudo-random number generator. Returns only the resulting
-- pseudo-random value.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> (runSTGen_ pureGen (\g -> applySTGen random g)) :: Int
-- 7879794327570578227
--
runSTGen_ :: RandomGen g => g -> (forall s. STGenM g s -> ST s a) -> a
-- | Frozen version of mutable TGenM generator
newtype TGen g
TGen :: g -> TGen g
[unTGen] :: TGen g -> g
-- | Wraps a TVar that holds a pure pseudo-random number generator.
newtype TGenM g
TGenM :: TVar g -> TGenM g
[unTGenM] :: TGenM g -> TVar g
-- | Creates a new TGenM in STM.
newTGenM :: g -> STM (TGenM g)
-- | Creates a new TGenM in IO.
newTGenMIO :: MonadIO m => g -> m (TGenM g)
-- | Applies a pure operation to the wrapped pseudo-random number
-- generator.
--
-- Examples
--
--
-- >>> import Control.Concurrent.STM
--
-- >>> import System.Random.Stateful
--
-- >>> import Data.Int (Int32)
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> stmGen <- newTGenMIO pureGen
--
-- >>> atomically $ applyTGen uniform stmGen :: IO Int32
-- 637238067
--
applyTGen :: (g -> (a, g)) -> TGenM g -> STM a
-- | The class of types for which a uniformly distributed value can be
-- drawn from all possible values of the type.
class Uniform a
-- | Generates a value uniformly distributed over all possible values of
-- that type.
--
-- There is a default implementation via Generic:
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> import System.Random.Stateful
--
-- >>> data MyBool = MyTrue | MyFalse deriving (Show, Generic, Finite, Uniform)
--
-- >>> data Action = Code MyBool | Eat (Maybe Bool) | Sleep deriving (Show, Generic, Finite, Uniform)
--
-- >>> gen <- newIOGenM (mkStdGen 42)
--
-- >>> uniformListM 10 gen :: IO [Action]
-- [Code MyTrue,Code MyTrue,Eat Nothing,Code MyFalse,Eat (Just False),Eat (Just True),Eat Nothing,Eat (Just False),Sleep,Code MyFalse]
--
uniformM :: (Uniform a, StatefulGen g m) => g -> m a
-- | Generates a value uniformly distributed over all possible values of
-- that type.
--
-- There is a default implementation via Generic:
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> import System.Random.Stateful
--
-- >>> data MyBool = MyTrue | MyFalse deriving (Show, Generic, Finite, Uniform)
--
-- >>> data Action = Code MyBool | Eat (Maybe Bool) | Sleep deriving (Show, Generic, Finite, Uniform)
--
-- >>> gen <- newIOGenM (mkStdGen 42)
--
-- >>> uniformListM 10 gen :: IO [Action]
-- [Code MyTrue,Code MyTrue,Eat Nothing,Code MyFalse,Eat (Just False),Eat (Just True),Eat Nothing,Eat (Just False),Sleep,Code MyFalse]
--
uniformM :: (Uniform a, StatefulGen g m, Generic a, GUniform (Rep a)) => g -> m a
-- | A definition of Uniform for Finite types. If your data
-- has several fields of sub-Word cardinality, this instance may
-- be more efficient than one, derived via Generic and
-- GUniform.
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> import System.Random.Stateful
--
-- >>> data Triple = Triple Word8 Word8 Word8 deriving (Show, Generic, Finite)
--
-- >>> instance Uniform Triple where uniformM = uniformViaFiniteM
--
-- >>> gen <- newIOGenM (mkStdGen 42)
--
-- >>> uniformListM 5 gen :: IO [Triple]
-- [Triple 60 226 48,Triple 234 194 151,Triple 112 96 95,Triple 51 251 15,Triple 6 0 208]
--
uniformViaFiniteM :: (StatefulGen g m, Generic a, GFinite (Rep a)) => g -> m a
-- | The class of types for which a uniformly distributed value can be
-- drawn from a range.
class UniformRange 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 <math>
-- - uniformRM (1 :: Float, 4 :: Float) generates values
-- uniformly from the set <math>
--
--
-- The following law should hold to make the function always defined:
--
--
-- uniformRM (a, b) = uniformRM (b, a)
--
--
-- The range is understood as defined by means of isInRange, so
--
--
-- isInRange (a, b) <$> uniformRM (a, b) gen == pure True
--
--
-- but beware of floating point number caveats.
--
-- There is a default implementation via Generic:
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> import Data.Word (Word8)
--
-- >>> import Control.Monad (replicateM)
--
-- >>> import System.Random.Stateful
--
-- >>> gen <- newIOGenM (mkStdGen 42)
--
-- >>> data Tuple = Tuple Bool Word8 deriving (Show, Generic, UniformRange)
--
-- >>> replicateM 10 (uniformRM (Tuple False 100, Tuple True 150) gen)
-- [Tuple False 102,Tuple True 118,Tuple False 115,Tuple True 113,Tuple True 126,Tuple False 127,Tuple True 130,Tuple False 113,Tuple False 150,Tuple False 125]
--
uniformRM :: (UniformRange a, StatefulGen g m) => (a, a) -> g -> m a
-- | A notion of (inclusive) ranges prescribed to a.
--
-- Ranges are symmetric:
--
--
-- isInRange (lo, hi) x == isInRange (hi, lo) x
--
--
-- Ranges include their endpoints:
--
--
-- isInRange (lo, hi) lo == True
--
--
-- When endpoints coincide, there is nothing else:
--
--
-- isInRange (x, x) y == x == y
--
--
-- Endpoints are endpoints:
--
--
-- isInRange (lo, hi) x ==>
-- isInRange (lo, x) hi == x == hi
--
--
-- Ranges are transitive relations:
--
--
-- isInRange (lo, hi) lo' && isInRange (lo, hi) hi' && isInRange (lo', hi') x
-- ==> isInRange (lo, hi) x
--
--
-- There is a default implementation of isInRange via
-- Generic. Other helper function that can be used for
-- implementing this function are isInRangeOrd and
-- isInRangeEnum
isInRange :: UniformRange a => (a, a) -> a -> Bool
-- | 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 <math>
-- - uniformRM (1 :: Float, 4 :: Float) generates values
-- uniformly from the set <math>
--
--
-- The following law should hold to make the function always defined:
--
--
-- uniformRM (a, b) = uniformRM (b, a)
--
--
-- The range is understood as defined by means of isInRange, so
--
--
-- isInRange (a, b) <$> uniformRM (a, b) gen == pure True
--
--
-- but beware of floating point number caveats.
--
-- There is a default implementation via Generic:
--
--
-- >>> :seti -XDeriveGeneric -XDeriveAnyClass
--
-- >>> import GHC.Generics (Generic)
--
-- >>> import Data.Word (Word8)
--
-- >>> import Control.Monad (replicateM)
--
-- >>> import System.Random.Stateful
--
-- >>> gen <- newIOGenM (mkStdGen 42)
--
-- >>> data Tuple = Tuple Bool Word8 deriving (Show, Generic, UniformRange)
--
-- >>> replicateM 10 (uniformRM (Tuple False 100, Tuple True 150) gen)
-- [Tuple False 102,Tuple True 118,Tuple False 115,Tuple True 113,Tuple True 126,Tuple False 127,Tuple True 130,Tuple False 113,Tuple False 150,Tuple False 125]
--
uniformRM :: (UniformRange a, StatefulGen g m, Generic a, GUniformRange (Rep a)) => (a, a) -> g -> m a
-- | A notion of (inclusive) ranges prescribed to a.
--
-- Ranges are symmetric:
--
--
-- isInRange (lo, hi) x == isInRange (hi, lo) x
--
--
-- Ranges include their endpoints:
--
--
-- isInRange (lo, hi) lo == True
--
--
-- When endpoints coincide, there is nothing else:
--
--
-- isInRange (x, x) y == x == y
--
--
-- Endpoints are endpoints:
--
--
-- isInRange (lo, hi) x ==>
-- isInRange (lo, x) hi == x == hi
--
--
-- Ranges are transitive relations:
--
--
-- isInRange (lo, hi) lo' && isInRange (lo, hi) hi' && isInRange (lo', hi') x
-- ==> isInRange (lo, hi) x
--
--
-- There is a default implementation of isInRange via
-- Generic. Other helper function that can be used for
-- implementing this function are isInRangeOrd and
-- isInRangeEnum
isInRange :: (UniformRange a, Generic a, GUniformRange (Rep a)) => (a, a) -> a -> Bool
-- | Utilize Ord instance to decide if a value is within the range.
-- Designed to be used for implementing isInRange
isInRangeOrd :: Ord a => (a, a) -> a -> Bool
-- | Utilize Enum instance to decide if a value is within the range.
-- Designed to be used for implementing isInRange
isInRangeEnum :: Enum a => (a, a) -> a -> Bool
-- | Generates a list of pseudo-random values.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> g <- newIOGenM pureGen
--
-- >>> uniformListM 10 g :: IO [Bool]
-- [True,True,True,True,False,True,True,False,False,False]
--
uniformListM :: (StatefulGen g m, Uniform a) => Int -> g -> m [a]
-- | Generates a list of pseudo-random values in a specified range.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> let pureGen = mkStdGen 137
--
-- >>> g <- newIOGenM pureGen
--
-- >>> uniformListRM 10 (20, 30) g :: IO [Int]
-- [23,21,28,25,28,28,26,25,29,27]
--
uniformListRM :: (StatefulGen g m, UniformRange a) => Int -> (a, a) -> g -> m [a]
-- | Shuffle elements of a list in a uniformly random order.
--
-- Examples
--
--
-- >>> import System.Random.Stateful
--
-- >>> runStateGen_ (mkStdGen 127) $ uniformShuffleListM "ELVIS"
-- "LIVES"
--
uniformShuffleListM :: StatefulGen g m => [a] -> g -> m [a]
-- | uniformByteArrayM n g generates a ByteArray of length
-- n filled with pseudo-random bytes.
uniformByteArrayM :: StatefulGen g m => Bool -> Int -> g -> m ByteArray
-- | Generates a pseudo-random ByteString of the specified size.
uniformByteStringM :: StatefulGen g m => Int -> g -> m ByteString
-- | uniformShortByteString n g generates a ShortByteString
-- of length n filled with pseudo-random bytes.
uniformShortByteStringM :: StatefulGen g m => Int -> g -> m ShortByteString
-- | Using an ST action that generates 8 bytes at a time fill in a
-- new ByteArray in architecture agnostic manner.
fillByteArrayST :: Bool -> Int -> ST s Word64 -> ST s ByteArray
-- | Efficiently fills in a new ShortByteString in a platform
-- independent manner.
-- | Deprecated: In favor of fillByteArrayST
genShortByteStringIO :: Int -> IO Word64 -> IO ShortByteString
-- | Same as genShortByteStringIO, but runs in ST.
-- | Deprecated: In favor of fillByteArrayST, since
-- uniformShortByteString, which it was used for, was also
-- deprecated
genShortByteStringST :: Int -> ST s Word64 -> ST s ShortByteString
-- | Efficiently generates a sequence of pseudo-random bytes in a platform
-- independent manner.
defaultUnsafeUniformFillMutableByteArray :: RandomGen g => MutableByteArray s -> Int -> Int -> g -> ST s g
-- | Generates uniformly distributed Double in the range
-- <math>. Numbers are generated by generating uniform
-- Word64 and dividing it by <math>. It's used to implement
-- UniformRange instance for Double.
uniformDouble01M :: forall g m. StatefulGen g m => g -> m Double
-- | Generates uniformly distributed Double in the range
-- <math>. Number is generated as <math>. Constant is 1/2 of
-- smallest nonzero value which could be generated by
-- uniformDouble01M.
uniformDoublePositive01M :: forall g m. StatefulGen g m => g -> m Double
-- | Generates uniformly distributed Float in the range
-- <math>. Numbers are generated by generating uniform
-- Word32 and dividing it by <math>. It's used to implement
-- UniformRange instance for Float.
uniformFloat01M :: forall g m. StatefulGen g m => g -> m Float
-- | Generates uniformly distributed Float in the range
-- <math>. Number is generated as <math>. Constant is 1/2 of
-- smallest nonzero value which could be generated by
-- uniformFloat01M.
uniformFloatPositive01M :: forall g m. StatefulGen g m => g -> m Float
-- | Generates uniformly distributed Enum. One can use it to define
-- a Uniform instance:
--
--
-- data Colors = Red | Green | Blue deriving (Enum, Bounded)
-- instance Uniform Colors where uniformM = uniformEnumM
--
uniformEnumM :: forall a g m. (Enum a, Bounded a, StatefulGen g m) => g -> m a
-- | Generates uniformly distributed Enum in the given range. One
-- can use it to define a UniformRange instance:
--
--
-- data Colors = Red | Green | Blue deriving (Enum)
-- instance UniformRange Colors where
-- uniformRM = uniformEnumRM
-- inInRange (lo, hi) x = isInRange (fromEnum lo, fromEnum hi) (fromEnum x)
--
uniformEnumRM :: forall a g m. (Enum a, StatefulGen g m) => (a, a) -> g -> m a
-- | Architecture specific Word generation in the specified lower
-- range
uniformWordR :: StatefulGen g m => Word -> g -> m Word
-- | This is the function that is used to scale a floating point value from
-- random word range to the custom [low, high] range.
scaleFloating :: forall a w. (RealFloat a, Integral w, Bounded w, FiniteBits w) => a -> a -> w -> a
instance Control.DeepSeq.NFData g => Control.DeepSeq.NFData (System.Random.Stateful.AtomicGen g)
instance Foreign.Storable.Storable g => Foreign.Storable.Storable (System.Random.Stateful.AtomicGen g)
instance System.Random.Internal.SplitGen g => System.Random.Internal.SplitGen (System.Random.Stateful.AtomicGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.RandomGen (System.Random.Stateful.AtomicGen g)
instance GHC.Show.Show g => GHC.Show.Show (System.Random.Stateful.AtomicGen g)
instance GHC.Classes.Ord g => GHC.Classes.Ord (System.Random.Stateful.AtomicGen g)
instance GHC.Classes.Eq g => GHC.Classes.Eq (System.Random.Stateful.AtomicGen g)
instance Control.DeepSeq.NFData g => Control.DeepSeq.NFData (System.Random.Stateful.IOGen g)
instance Foreign.Storable.Storable g => Foreign.Storable.Storable (System.Random.Stateful.IOGen g)
instance System.Random.Internal.SplitGen g => System.Random.Internal.SplitGen (System.Random.Stateful.IOGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.RandomGen (System.Random.Stateful.IOGen g)
instance GHC.Show.Show g => GHC.Show.Show (System.Random.Stateful.IOGen g)
instance GHC.Classes.Ord g => GHC.Classes.Ord (System.Random.Stateful.IOGen g)
instance GHC.Classes.Eq g => GHC.Classes.Eq (System.Random.Stateful.IOGen g)
instance Control.DeepSeq.NFData g => Control.DeepSeq.NFData (System.Random.Stateful.STGen g)
instance Foreign.Storable.Storable g => Foreign.Storable.Storable (System.Random.Stateful.STGen g)
instance System.Random.Internal.SplitGen g => System.Random.Internal.SplitGen (System.Random.Stateful.STGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.RandomGen (System.Random.Stateful.STGen g)
instance GHC.Show.Show g => GHC.Show.Show (System.Random.Stateful.STGen g)
instance GHC.Classes.Ord g => GHC.Classes.Ord (System.Random.Stateful.STGen g)
instance GHC.Classes.Eq g => GHC.Classes.Eq (System.Random.Stateful.STGen g)
instance Control.DeepSeq.NFData g => Control.DeepSeq.NFData (System.Random.Stateful.TGen g)
instance Foreign.Storable.Storable g => Foreign.Storable.Storable (System.Random.Stateful.TGen g)
instance System.Random.Internal.SplitGen g => System.Random.Internal.SplitGen (System.Random.Stateful.TGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.RandomGen (System.Random.Stateful.TGen g)
instance GHC.Show.Show g => GHC.Show.Show (System.Random.Stateful.TGen g)
instance GHC.Classes.Ord g => GHC.Classes.Ord (System.Random.Stateful.TGen g)
instance GHC.Classes.Eq g => GHC.Classes.Eq (System.Random.Stateful.TGen g)
instance System.Random.Seed.SeedGen g => System.Random.Seed.SeedGen (System.Random.Stateful.TGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.FrozenGen (System.Random.Stateful.TGen g) GHC.Conc.Sync.STM
instance System.Random.Internal.RandomGen g => System.Random.Internal.ThawedGen (System.Random.Stateful.TGen g) GHC.Conc.Sync.STM
instance System.Random.Internal.RandomGen r => System.Random.Stateful.RandomGenM (System.Random.Stateful.TGenM r) r GHC.Conc.Sync.STM
instance System.Random.Internal.RandomGen g => System.Random.Internal.StatefulGen (System.Random.Stateful.TGenM g) GHC.Conc.Sync.STM
instance System.Random.Seed.SeedGen g => System.Random.Seed.SeedGen (System.Random.Stateful.STGen g)
instance System.Random.Internal.RandomGen g => System.Random.Internal.FrozenGen (System.Random.Stateful.STGen g) (GHC.ST.ST s)
instance System.Random.Internal.RandomGen g => System.Random.Internal.ThawedGen (System.Random.Stateful.STGen g) (GHC.ST.ST s)
instance System.Random.Internal.RandomGen r => System.Random.Stateful.RandomGenM (System.Random.Stateful.STGenM r s) r (GHC.ST.ST s)
instance System.Random.Internal.RandomGen g => System.Random.Internal.StatefulGen (System.Random.Stateful.STGenM g s) (GHC.ST.ST s)
instance System.Random.Seed.SeedGen g => System.Random.Seed.SeedGen (System.Random.Stateful.IOGen g)
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.FrozenGen (System.Random.Stateful.IOGen g) m
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.ThawedGen (System.Random.Stateful.IOGen g) m
instance (System.Random.Internal.RandomGen r, Control.Monad.IO.Class.MonadIO m) => System.Random.Stateful.RandomGenM (System.Random.Stateful.IOGenM r) r m
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.StatefulGen (System.Random.Stateful.IOGenM g) m
instance System.Random.Seed.SeedGen g => System.Random.Seed.SeedGen (System.Random.Stateful.AtomicGen g)
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.FrozenGen (System.Random.Stateful.AtomicGen g) m
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.ThawedGen (System.Random.Stateful.AtomicGen g) m
instance (System.Random.Internal.RandomGen r, Control.Monad.IO.Class.MonadIO m) => System.Random.Stateful.RandomGenM (System.Random.Stateful.AtomicGenM r) r m
instance (System.Random.Internal.RandomGen g, Control.Monad.IO.Class.MonadIO m) => System.Random.Internal.StatefulGen (System.Random.Stateful.AtomicGenM g) m
instance (System.Random.Internal.RandomGen r, Control.Monad.State.Class.MonadState r m) => System.Random.Stateful.RandomGenM (System.Random.Internal.StateGenM r) r m