A monad capable of producing random values of a.

Evaluate a random computation with the given initial generator and return the final value.

Turn a standard RNG function into a Random monad.

Allows a Random monad to be evaluated lazily.

This function should be applied with care, otherwise a strict Random monad could still force early evaluation of this lazy version.

Generate value using a random seed with the Standard PCG Generator.

>>> import MCSP.System.Random.Monad (liftRandom)
>>> import Data.Function (id)
>>> import System.Random.PCG.Class (uniform1)
>>> generate (liftRandom (uniform1 id))
...

Generate value using a random seed with the Fast PCG Generator.

>>> import MCSP.System.Random.Monad (liftRandom)
>>> import Data.Function (id)
>>> import System.Random.PCG.Class (uniform1)
>>> generateFast (liftRandom (uniform1 id))
...

Values used to seed a random number generator.

Parser combinator for reading seeds.

Read a seed in hexadecimal format.

Inverse of showSeed.

>>> readSeed "0 1"
(0,1)

>>> readSeed (showSeed (5, 10))
(5,10)

>>> readSeed " 75f9fea579c63117 8a3a15e4c0a7029f "
(8501105758304612631,9960297598112170655)

ShowS implementation for hexadecimal seed.

String representing the RNG seed in hexadecimal.

Inverse of readSeed.

>>> showSeed (0, 1)
"0 1"

Use given seed to generate value using the Fast PCG generator.

>>> import MCSP.System.Random.Monad (liftRandom)
>>> import Data.Function (id)
>>> import System.Random.PCG.Class (uniform1)
>>> generateFastWith (100,200) (liftRandom (uniform1 id))
77824

Use given seed to generate value using the Standard PCG generator.

>>> import MCSP.System.Random.Monad (liftRandom)
>>> import Data.Function (id)
>>> import System.Random.PCG.Class (uniform1)
>>> generateWith (100,200) (liftRandom (uniform1 id))
717541362

Generate a new random seed.

>>> randomSeed
...

O(1) Generate a uniformly distributed random variate.

• Use entire range for integral types.
• Use (0,1] range for floating types.

>>> import Prelude (Double)
>>> generateWith @Double (1,2) uniform
0.6502342391751404

O(1) Generate a uniformly distributed random variate in the given range.

• Use inclusive range for integral types.
• Use (a,b] range for floating types.

>>> generateWith (1,2) $ uniformR @Int 10 50
16

O(1) Generate a uniformly distributed random variate in the range [0,b).

• For integral types the bound must be less than the max bound of Word32 (4294967295). Behaviour is undefined for negative bounds.

>>> generateWith (1,2) $ uniformB @Int 200
143

O(1) Generate a uniformly distributed random variate.

It should be equivalent to uniformR (minBound, maxBound), but for non-Variate.

>>> import Prelude (Show)
>>> data T = A | B | C | D deriving (Enum, Bounded, Show)
>>> generateWith @T (1,3) uniformE
C

O(1) Generate a uniformly distributed random variate in the given inclusive range.

It should be equivalent to uniformR, but for non-Variate.

>>> generateWith (1,3) $ uniformRE @Int 0 10
5

>>> generateWith (1,3) $ uniformRE 'a' 'z'
'n'

O(1) Choose a single random value from a non-empty list.

>>> generateWith (1,2) $ choice ["hi", "hello", "ola"]
"hello"

O(n) Choose a single random value from a non-empty with probability proportional to weight.

>>> import Control.Monad (replicateM)
>>> generateWith (1,2) $ replicateM 10 $ weightedChoice [(1, "hi"), (10, "hello")]
["hello","hello","hello","hello","hi","hello","hello","hello","hello","hello"]

O(?) Shuffles a list randomly.

>>> generateWith (1,2) $ shuffle @[Int] [1..5]
[4,1,2,3,5]

O(n) Generate random partitions of a vector.

>>> generateWith (1,4) $ partitions @Vector @Int [1..10]
[[1,2,3],[4,5,6,7,8],[9],[10]]

Generates an infinite list of random values.

>>> import Prelude (take, (<$>))
>>> generateWith (1,3) (take 3 <$> repeatR (uniform @Int))
[-8858759364290496978,-2446124676014956441,1387719708863309077]

Random version of iterate.

>>> import Prelude ((<$>), (.), liftA2)
>>> import Data.List.NonEmpty (take)
>>> generateWith (1,3) $ take 3 <$> iterateR (liftA2 (+) uniform . pure) 2
[2.0,2.0197656927151786,2.3871610084947057]