Note: this is the stubbed version of module RandomGen. Try to figure out how to fill in all parts of this file marked undefined. CIS 5520 students should be able to access this code through github. Eventually, the completed version will be available.

In class exercise: Random Generation

> module RandomGen where

> -- Some library operations that you can use for this exercise.
> import qualified Control.Monad as Monad 
> import System.Random (StdGen)
> import qualified System.Random as Random (mkStdGen, uniform, uniformR, randomIO)

> -- Make sure you have filled in all of the 'undefined' values in the State module.
> -- If you have not, modify the State import below to Control.Monad.State
> -- but don't import both State and Control.Monad.State
> import qualified State as S

> -- It also might be tempting to import Test.QuickCheck, but do not import anything
> -- from quickcheck for this exercise.

Random Generation

Recall that QuickCheck needs to randomly generate values of any type. It turns out that we can use the state monad to define something like the Gen monad used in the QuickCheck libary.

First, a brief discussion of pseudo-random number generators. Pseudo-random number generators aren't really random, they just look like it. They are more like functions that are so complicated that they might as well be random. The nice property about them is that they are repeatable, if you give them the same seed they produce the same sequence of "random" numbers.

Haskell has a library for Pseudo-Random numbers called System.Random. It features the following elements:

type StdGen  -- A type for a "standard" random number generator.
             -- Keeps track of the current seed.

-- | Construct a generator from a given seed. Distinct arguments
-- are likely to produce distinct generators.
mkStdGen :: Int -> StdGen

-- The `uniform` function is overloaded, but we will only use two instances of
-- it today.

> -- | Returns an Int that is uniformly distributed in a range of at least 30 bits.
> uniformInt  :: StdGen -> (Int, StdGen)
> uniformInt = Random.uniform

> -- | Returns True / False with even chances
> uniformBool  :: StdGen -> (Bool, StdGen)
> uniformBool = Random.uniform

Side note: the default constructor mkStdGen is a bit weak so we wrap it to perturb the seed a little first:

> mkStdGen :: Int -> StdGen
> mkStdGen = Random.mkStdGen . (* (3::Int) ^ (20::Int))

For example, we can generate a random integer by constructing a random number generator, calling uniform and then projecting the result.

> testRandom :: Int -> Int
> testRandom i = fst (uniformInt (mkStdGen i))

Our random integers depend on the seed that we provide. Make sure that you get different numbers from these three calls.

> -- >>> testRandom 1

> -- >>> testRandom 2

> -- >>> testRandom 3

But we can also produce several different random Ints by using the output of one call to Random.uniform as the input to the next.

> (int1 :: Int, stdgen1) = uniformInt (mkStdGen 1)
> (int2 :: Int, stdgen2) = uniformInt stdgen1
> (int3 :: Int , _) = uniformInt stdgen2

> -- >>> int1
> 
> -- >>> int2
> 
> -- >>> int3

If we'd like to constrain that integer to a specific range (0, n) we can use the mod operation.

> nextBounded :: Int -> StdGen -> (Int, StdGen)
> nextBounded bound s = let (x,s1) = uniformInt s in (x `mod` bound, s1)

These tests should all produce random integers between 0 and 20.

> testBounded :: Int -> Int
> testBounded = fst . nextBounded 20 . mkStdGen

> -- >>> testBounded 1

> -- >>> testBounded 2

> -- >>> testBounded 3

QuickCheck is defined by a class of types that can construct random values. Let's do it first the hard way... i.e. by explicitly passing around the state of the random number generator.

> -- | Extract random values of any type
> class Arb1 a where
>    arb1 :: StdGen -> (a, StdGen)

> instance Arb1 Int where
>    arb1 :: StdGen -> (Int, StdGen)
>    arb1 = uniformInt

> instance Arb1 Bool where
>    arb1 :: StdGen -> (Bool, StdGen)
>    arb1 = uniformBool

With this class, we can also generalize our "testing" function.

> testArb1 :: Arb1 a => Int -> a
> testArb1 = fst . arb1 . mkStdGen

What about for pairs? Note that Haskell needs the type annotations for the two calls to arb1 to resolve ambiguity.

> instance (Arb1 a, Arb1 b) => Arb1 (a, b) where
>    arb1 :: StdGen -> ((a,b), StdGen)
>    arb1 s = let (a :: a, s1) = arb1 s
>                 (b :: b, s2) = arb1 s1
>             in ((a,b), s2)

Try out this definition, noting the different integers in the two components in the pair. If both calls to arb1 above used s, then we'd get the same number in both components.

> -- >>> testArb1 1 :: (Int, Int)
> 
> -- >>> testArb1 2 :: (Int, Int)

How about for the Maybe type? Use the arb1 instance for the Bool type above to generate a random boolean and then test it to decide whether you should return Nothing or Just a, where the a also comes from arb1.

> instance (Arb1 a) => Arb1 (Maybe a) where
>    arb1 :: StdGen -> (Maybe a, StdGen)
>    arb1 s  = undefined

And for lists? Give this one a try! Although we don't have QCs combinators available, you should be able to control the frequency of when cons and nil is generated so that you get reasonable lists.

> instance Arb1 a => Arb1 [a] where
>   arb1 s = undefined

> 
> -- >>> testArb1 1 :: [Int]

> -- >>> testArb1 2 :: [Int]

> -- >>> testArb1 3 :: [Int]

Ouch, there's a lot of state passing going on here.

State Monad to the Rescue

Previously, we have developed a reusable library for the State monad. Let's use it to define a generator monad for QuickCheck.

Our reusable library defines an abstract type for the state monad, and the following operations for working with these sorts of computations.

type State s a = ...

instance Monad (State s) where ...

get      :: State s s
put      :: s -> State s ()

runState :: State s a -> s -> (a,s)

Now let's define a type for generators, using the state monad.

> type Gen a = S.State StdGen a

With this type, we can create a type class similar to the one in the QuickCheck library.

> class Arb a where
>   arb :: Gen a

For example, we can use the operations on the state monad to access and update the random number generator stored in the State StdGen a type.

> instance Arb Int where
>   arb :: Gen Int
>   arb = do
>     s <- S.get
>     let (y :: Int, s') = Random.uniform s
>     S.put s'
>     return y

What if we want a bounded generator? See if you can define one without using Random.uniformR.

> bounded :: Int -> Gen Int
> bounded b = undefined

Now define a sample function, which generates and prints 10 random values.

> sample :: Show a => Gen a -> IO ()
> sample gen = do
>   seed <- (Random.randomIO :: IO Int) -- get a seed from the global random number generator
>                                       -- hidden in the IO monad
>   undefined

For example, you should be able to sample using the bounded combinator.

ghci> sample (bounded 10)
5
9
0
5
4
6
0
0
7
6

What about random generation for other types? How does the state monad help that definition? How does it compare to the version above?

> instance (Arb a, Arb b) => Arb (a,b) where
>  arb = undefined

Can we define some standard QuickCheck combinators to help us? What about elements, useful for the Bool instance ?

> elements :: [a] -> Gen a
> elements = undefined

> instance Arb Bool where
>   arb :: Gen Bool
>   arb = elements [False, True]

or frequency, which we can use for the [a] instance ?

> frequency :: [(Int, Gen a)] -> Gen a
> frequency = undefined

> instance (Arb a) => Arb [a] where
>   arb :: Arb a => Gen [a]
>   arb = frequency [(1, return []), (3, (:) <$> arb <*> arb)]

CIS 5520: Advanced Programming

Fall 2024

In class exercise: Random Generation

Random Generation

State Monad to the Rescue