tuturto talks how to generate random numbers (and other values) in Haskell
Hosted by tuturto on 2019-07-03 is flagged as Clean and is released under a CC-BY-SA license.
Tags: haskel, random numbers.
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Part of the series: Haskell
A series looking into the Haskell (programming language)
There’s lots of random and similar sounding words in this episode. I hope you can still follow what I’m trying to explain, but I’m aware that it might be hard.
Haskell functions are pure, meaning that they will always produce same values for same set of arguments. This might sound hard when you want to generate random numbers, but it turns out that the solution isn’t too tricky.
First part to the puzzle is type class
class RandomGen g where
next :: g -> (Int, g)
genRange :: g -> (Int, Int)
split :: g -> (g, g)
next produces tuple, where first element is random
Int and second element is new random generator.
genRange returns tuple defining minimum and maximum values this generator will return.
split produces tuple with two new random generators.
RandomGen to produce random values of specific type or for specific range requires a bit of arithmetic. It’s easier to use
Random that defines functions for that specific task:
class Random a where
randomR :: RandomGen g => (a, a) -> g -> (a, g)
random :: RandomGen g => g -> (a, g)
randomRs :: RandomGen g => (a, a) -> g -> [a]
randoms :: RandomGen g => g -> [a]
randomRIO :: (a, a) -> IO a
randomIO :: IO a
randomR, when given range and random generator, produces tuple with random number and new generator
random, is similar but doesn’t take range. Instead it will use minimum and maximum specific to that data type
randomRs, takes range and produces infinite list of random values within that range
randoms, simply produces infinite list of random values using range that is specific to datatype
randomIO are effectful versions that don’t need random generator, but use some default one
RandomGen is source of randomness and
Random is datatype specific way of generating random values using random generator
Final part of the puzzle is where to get
RandomGen? One could initialize one manually, but then it wouldn’t be random. However, there’s function
getStdGen that will seed
RandomGen using OS default random number generator, current time or some other method. Since it has signature of
getStdGen :: IO StdGen, one can only call it in IO monad.
Functions that operate with IO can only be called from other IO functions. They can call pure functions, but pure functions can’t call them. So there’s two options: have the code that needs random numbers in effectful function or get
RandomGen in effectful function and pass it to pure function.
-- | get n unique entries from given list in random order
-- | if n > length of list, all items of the list will be returned
getR :: RandomGen g => g -> Int -> [a] -> [a]
getR g n xs =
fmap (xs !!) ids
ids = take (min n $ length xs) $ nub $ randomRs (0, length xs - 1) g
-- | Returns 4 unique numbers between 1 and 10 (inclusive)
test :: IO [Int]
test = do
g <- getStdGen
return $ getR g 4 [1..10]
Pseudo randomness doesn’t require IO, only seeding the generator does. Simple computation that don’t require many calls to
random are easy enough. If you need lots of random values,
MonadRandom is better suited. It takes care of carrying implicit
RandomGen along while your computation progresses.
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