##### Views

A simple monad transformer to allow computations in the transformed monad to generate random values.

## 1 The code

```{-# OPTIONS_GHC -fglasgow-exts #-}

getRandom,
getRandomR,
getRandoms,
getRandomRs,
evalRandT,
evalRand,
evalRandIO,
fromList,
Rand, RandT -- but not the data constructors
) where

import System.Random
import Control.Arrow

getRandom :: (Random a) => m a
getRandoms :: (Random a) => m [a]
getRandomR :: (Random a) => (a,a) -> m a
getRandomRs :: (Random a) => (a,a) -> m [a]

newtype (RandomGen g) => RandT g m a = RandT (StateT g m a)

liftState :: (MonadState s m) => (s -> (a,s)) -> m a
liftState t = do v <- get
let (x, v') = t v
put v'
return x

getRandom = RandT . liftState \$ random
getRandoms = RandT . liftState \$ first randoms . split
getRandomR (x,y) = RandT . liftState \$ randomR (x,y)
getRandomRs (x,y) = RandT . liftState \$
first (randomRs (x,y)) . split

evalRandT :: (Monad m, RandomGen g) => RandT g m a -> g -> m a
evalRandT (RandT x) g = evalStateT x g

runRandT  :: (Monad m, RandomGen g) => RandT g m a -> g -> m (a, g)
runRandT (RandT x) g = runStateT x g

newtype Rand g a = Rand (RandT g Identity a)

evalRand :: (RandomGen g) => Rand g a -> g -> a
evalRand (Rand x) g = runIdentity (evalRandT x g)

runRand :: (RandomGen g) => Rand g a -> g -> (a, g)
runRand (Rand x) g = runIdentity (runRandT x g)

evalRandIO :: Rand StdGen a -> IO a
evalRandIO (Rand (RandT x)) = getStdRandom (runIdentity . runStateT x)

fromList :: (MonadRandom m) => [(a,Rational)] -> m a
fromList [] = error "MonadRandom.fromList called with empty list"
fromList [(x,_)] = return x
fromList xs = do let s = fromRational \$ sum (map snd xs) -- total weight
cs = scanl1 (\(x,q) (y,s) -> (y, s+q)) xs -- cumulative weight
p <- liftM toRational \$ getRandomR (0.0,s)
return . fst . head \$ dropWhile (\(x,q) -> q < p) cs```

To make use of common transformer stacks involving Rand and RandT, the following definitions may prove useful:

```instance (MonadRandom m) => MonadRandom (StateT s m) where
getRandom = lift getRandom
getRandomR r = lift \$ getRandomR r

getRandom = lift getRandom
getRandomR r = lift \$ getRandomR r

getRandom = lift getRandom
getRandomR r = lift \$ getRandomR r

instance (MonadState s m, RandomGen g) => MonadState s (RandT g m) where
get = lift get
put = lift . put

local f (RandT m) = RandT \$ local f m

instance (MonadWriter w m, RandomGen g, Monoid w) => MonadWriter w (RandT g m) where
tell = lift . tell
listen (RandT m) = RandT \$ listen m
pass (RandT m) = RandT \$ pass m```

You may also want a MonadRandom instance for IO:

```instance MonadRandom IO where
getRandom = randomIO
getRandomR = randomRIO```

## 2 Connection to stochastics

There is some correspondence between notions in programming and in mathematics:

 random generator ~ random variable / probabilistic experiment result of a random generator ~ outcome of a probabilistic experiment

Thus the signature

`rx :: (MonadRandom m, Random a) => m a`
can be considered as "
rx
is a random variable". In the do-notation the line
`x <- rx`
means that "
x
is an outcome of
rx
".

In a language without higher order functions and using a random generator "function" it is not possible to work with random variables, it is only possible to compute with outcomes, e.g. `rand()+rand()`. In a language where random generators are implemented as objects, computing with random variables is possible but still cumbersome.

In Haskell we have both options either computing with outcomes

```   do x <- rx
y <- ry
return (x+y)```

or computing with random variables

`   liftM2 (+) rx ry`
This means that
liftM
like functions convert ordinary arithmetic into

random variable arithmetic. But there is also some arithmetic on random variables which can not be performed on outcomes. For example, given a function that repeats an action until the result fulfills a certain property (I wonder if there is already something of this kind in the standard libraries)

```  untilM :: Monad m => (a -> Bool) -> m a -> m a
untilM p m =
do x <- m
if p x then return x else untilM p m```

we can suppress certain outcomes of an experiment. E.g. if

`  getRandomR (-10,10)`

is a uniformly distributed random variable between -10 and 10, then

`  untilM (0/=) (getRandomR (-10,10))`

is a random variable with a uniform distribution of $\{-10, \dots, -1, 1, \dots, 10\}$.