### Benjamin Kovach

Haskell Bits is a new series of bite-sized posts that I hope will empower people to “get it done and move on”, providing useful information and links to learn more if desired. I’ll be providing full main files in each example (with imports!) to make porting this stuff into your own project as frictionless as possible. This first “Haskell Bit” will cover randomness.

You need at least two things to produce a random number:

• An initial “seed” value
• A pure function that produces a new number from that seed. (“RNG”)

That’s all for a single number.

Most programming languages will hide these details from you unless you need them. Most of the time, you can just call random() and get a random number (typically between 0 and 1), using a seed value generated from some system variable that is always changing (current time in very small units is common).

The simplest way to replicate this behavior in Haskell is by using the System.Random module, part of the random package.

We can use randomIO and randomRIO to pull from a global RNG:

import System.Random

main :: IO ()
main = do
-- A random Double between 0 and 1
(randomIO :: IO Double) >>= print

-- A random Int between 1 and 6 (A die roll)
randomRIO (1, 6) >>= print

This is pretty much the interface that most other languages start with. Better would be to separate out IO as much as possible from the inevitable rest of our program. We can do that by confining IO usage to one operation: coming up with an initial RNG.

import System.Random

dieRoll :: RandomGen g => g -> (Int, g)
dieRoll = randomR (1, 6)

main :: IO ()
main = do
-- New generator, generated from the system RNG
gen <- newStdGen

let (result, newGen) = dieRoll gen
print result

let (newResult, newNewGen) = dieRoll newGen
print newResult

We don’t want to duplicate this code every time we want to add a new die roll. The next logical step would be to sprinkle in some State to store the current RNG in:

import System.Random

dieRoll :: RandomGen g => State g Int
dieRoll = state (randomR (1, 6))

twoDice :: RandomGen g => State g Int
twoDice = (+) <$> dieRoll <*> dieRoll main :: IO () main = do gen <- newStdGen print (evalState twoDice gen) Now we can run more complex programs that employ random numbers. Note that newStdGen can be replaced with mkStdGen :: Int -> StdGen if you want to provide an integral seed instead of using the global StdGen. You can avoid some of the state boilerplate and get a few more benefits by bringing in the MonadRandom package. Here’s some code that accomplishes the same goal using MonadRandom: import System.Random import Control.Monad.Random dieRoll :: RandomGen g => Rand g Int dieRoll = getRandomR (1, 6) twoDice :: RandomGen g => Rand g Int twoDice = (+) <$> dieRoll <*> dieRoll

main :: IO ()
main = do
gen <- newStdGen
print (evalRand twoDice gen)

Apart from providing a nice way to write (slightly) terser randomness code, MonadRandom is more explicit about the domain we’re working in, and ships with a couple of killer utilities; namely, the minimalistic sampling functions uniform and fromList (also weighted from MonadRandom 0.5). This program, for example, generates a list of 20 moves that might come up in a Dance Dance Revolution song:

import System.Random

data Direction = U | D | L | R deriving (Show, Eq)

step :: RandomGen g => Rand g Direction
step = uniform [U,D,L,R]

stepWeighted :: RandomGen g => Rand g Direction
stepWeighted =
fromList [(U, 1), (D, 1), (L, 50), (R, 100)]

danceDanceRevolutionScroll :: RandomGen g => Rand g [Direction]
danceDanceRevolutionScroll = replicateM 20 \$ do
weightIt <- uniform [True, False]
if weightIt then stepWeighted else step

main :: IO ()
main = do
gen <- newStdGen
print (evalRand danceDanceRevolutionScroll gen)

fromList lets you specify weights for your random elements. L and R will probably show up a lot more than the other two directions when this is run.

MonadRandom supplies some other conveniences as well, but it’s not crazy stuffed with functionality. It’s a nice package that contains the minimal amount of code to be useful but not overengineered.

That said, sometimes you need more. First off, what about different distributions? The normal distribution is a pretty common necessity. random-fu really shines in this domain. You’ll have to pull in the rvar package as well to run this next example, which will print out a random number pulled from a normal distribution with mean 100 and a standard deviation of 5:

import System.Random
import Data.Random
import Data.RVar

normalNumber :: State StdGen Double
normalNumber = sampleRVar (normal 100 5)

main :: IO ()
main = do
gen <- newStdGen
print (evalState normalNumber gen)

Notice the State pattern from earlier. Also, there a bunch of common distributions that ship with random-fu.

One last thing I should mention is that we’re not tied to StdGen, the RNG that ships with random.

In fact, it does not have strong statistical properties, and should probably be avoided for many “real” applications (See this reddit post, and thank you to reddit user tom-md for the note!).

There are faster and more stable ones ones, like PureMT from random-source or TFGen from tf-random. These are both instances of RandomGen, so you can plug either one of those in wherever you saw the generic type signature RandomGen g => ... in this post. For example, mixing PureMT back into MonadRandom: