I can write the following:
f :: [Int] -> [Int]
f x = 0:(map (+1) x)
g :: [Int] -> [Int]
g x = map (*2) x
a = f b
b = g a
main = print $ take 5 a
And things work perfectly fine (ideone).
However, lets say I want g to do something more complex than multiply by 2, like ask the user for a number and add that, like so:
g2 :: [Int] -> IO [Int]
g2 = mapM (\x -> getLine >>= (return . (+x) . read))
How do I then, well, tie the knot?
Clarification:
Basically I want the list of Ints from f to be the input of g2 and the list of Ints from g2 to be the input of f.
The effectful generalization of lists is ListT:
import Control.Monad
import Pipes
f :: ListT IO Int -> ListT IO Int
f x = return 0 `mplus` fmap (+ 1) x
g2 :: ListT IO Int -> ListT IO Int
g2 x = do
n <- x
n' <- lift (fmap read getLine)
return (n' + n)
a = f b
b = g2 a
main = runListT $ do
n <- a
lift (print n)
mzero
You can also implement take like functionality with a little extra code:
import qualified Pipes.Prelude as Pipes
take' :: Monad m => Int -> ListT m a -> ListT m a
take' n l = Select (enumerate l >-> Pipes.take n)
main = runListT $ do
n <- take' 5 a
lift (print n)
mzero
Example session:
>>> main
0
1<Enter>
2
2<Enter>
3<Enter>
7
4<Enter>
5<Enter>
6<Enter>
18
7<Enter>
8<Enter>
9<Enter>
10<Enter>
38
You can learn more about ListT by reading the pipes tutorial, specifically the section on ListT.
Related
I was recently in need of putting head in between two monadic operations. Here's the SSCCE:
module Main where
f :: IO [Int]
f = return [1..5]
g :: Int -> IO ()
g = print
main = do
putStrLn "g <$> head <$> f"
g <$> head <$> f
putStrLn "g . head <$> f"
g . head <$> f
putStrLn "head <$> f >>= g"
head <$> f >>= g
This program is well-formed and compiles without warnings. However, only one version out of 3 above works1. Why is that?
And specifically, what would be the best way to link f and g together with head in the middle? I ended up using the 3rd one (in the form of do notation), but I don't really like it, since it should be a trivial one-liner2.
1 Spoiler alert: the 3rd one is the only one that prints 1; the other two are silent, both under runhaskell and repl.
2 I do realize that those are all one-liners, but the order of operations feels really confusing in the only one that works.
Probably the best way to write this is:
f >>= g . head
or in a more verbose form:
f >>= (g . head)
so we basically perform an fmap on the value for f (we thus take the head of the values wrapped in the IO monad), and then we pass then to g, like:
(head <$> f) >>= g
is semantically the same.
But now what happens if we use g <$> head <$> f? Let us first analyze the types:
f :: IO [Int]
g :: Int -> IO ()
(<$>) :: Functor m => (a -> b) -> m a -> m b
(I used m here to avoid confusion with the f function)
The canonical form of this is:
((<$>) ((<$>) g head) f)
The second (<$>) takes a g :: Int -> IO () and head :: [c] -> c as parameters, so that means that a ~ Int, b ~ IO (), and m ~ (->) [c]. So the result is:
(<$>) g head :: (->) [c] (IO ())
or less verbose:
g <$> head :: [c] -> IO ()
The first (<$>) function thus takes as parameters g <$> head :: [c] -> IO (), and IO [Int], so that means that m ~ IO, a ~ [Int], c ~ Int, b ~ IO (), and hence we obtain the type:
(<$>) (g <$> head) f :: IO (IO ())
We thus do not perform any real action: we fmap the [Int] list to an IO action (that is wrapped in the IO). You could see it as return (print 1): you do not "evaluate" the print 1, but you return that wrapped in an IO.
You can of course "absorb" the outer IO here, and then use the inner IO, like:
evalIO :: IO (IO f) -> IO f
evalIO res = do
f <- res
f
or shorter:
evalIO :: IO (IO f) -> IO f
evalIO res = res >>= id
(this can be generalized to all sorts of Monads, but this is irrelevant here).
The evalIO is also known as join :: Monad m => m (m a) -> m a.
The first and second are exactly the same, because <$> is left-associative and head is a function, and <$> is . in the function monad. Then,
g . head <$> f
= fmap (print . head) (return [1..5] :: IO [Int])
= do { x <- (return [1..5] :: IO [Int])
; return ( print (head x) ) }
= do { let x = [1..5]
; return ( print (head x) ) } :: IO _whatever
=
return ( print 1 ) :: IO (IO ())
We have one too many returns there. In fact,
= fmap (print . head) (return [1..5] :: IO [Int])
= return (print (head [1..5]))
= return (print 1)
is a shorter derivation.
The third one is
(head <$> f) >>= g
= (fmap head $ return [1..5]) >>= print
= (return (head [1..5])) >>= print
= (return 1) >>= print
which is obviously OK.
I have a function that pattern matches on its arguments to produce a computation in StateT () Maybe (). This computation can fail when run, in which case I want the current pattern match branch to fail, so to speak.
I highly doubt it's possible to have something like
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f (Just n1) (Just n2) = do
m <- compute (n1 + n2)
guard (m == 42)
f (Just n) _ = do
m <- compute n
guard (m == 42)
f _ (Just n) = do
m <- compute n
guard (m == 42)
behave in the way I want it to: When the first computation fails due to the guard or somewhere in compute, I want f to try the next pattern.
Obviously the above can't work, because StateT (as any other monad might) involves an additional parameter when expanded, so I probably can't formulate this as simple pattern guards.
The following does what I want, but it's ugly:
f' :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f' a b = asum (map (\f -> f a b) [f1, f2, f3])
where
f1 a b = do
Just n1 <- pure a
Just n2 <- pure b
m <- compute (n1 + n2)
guard (m == 42)
f2 a _ = do
Just n <- pure a
m <- compute n
guard (m == 42)
f3 _ b = do
Just n <- pure b
m <- compute n
guard (m == 42)
A call like execStateT (f (Just 42) (Just 1)) () would fail for f but return Just () for f', because it matches f2.
How do I get the behavior of f' while having elegant pattern matching with as little auxiliary definitions as possible like in f? Are there other, more elegant ways to formulate this?
Complete runnable example:
#! /usr/bin/env stack
-- stack --resolver=lts-11.1 script
import Control.Monad.Trans.State
import Control.Applicative
import Control.Monad
import Data.Foldable
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f (Just n1) (Just n2) = do
m <- compute (n1 + n2)
guard (m == 42)
f (Just n) _ = do
m <- compute n
guard (m == 42)
f _ (Just n) = do
m <- compute n
guard (m == 42)
f' :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f' a b = asum (map (\f -> f a b) [f1, f2, f3])
where
f1 a b = do
Just n1 <- pure a
Just n2 <- pure b
m <- compute (n1 + n2)
guard (m == 42)
f2 a _ = do
Just n <- pure a
m <- compute n
guard (m == 42)
f3 _ b = do
Just n <- pure b
m <- compute n
guard (m == 42)
main = do
print $ execStateT (f (Just 42) (Just 1)) () -- Nothing
print $ execStateT (f' (Just 42) (Just 1)) () -- Just (), because `f2` succeeded
Edit: I elicited quite some clever answers with this question so far, thanks! Unfortunately, they mostly suffer from overfitting to the particular code example I've given. In reality, I need something like this for unifying two expressions (let-bindings, to be precise), where I want to try unifying the RHS of two simultaneous lets if possible and fall through to the cases where I handle let bindings one side at a time by floating them. So, actually there's no clever structure on Maybe arguments to exploit and I'm not computeing on Int actually.
The answers so far might benefit others beyond the enlightenment they brought me though, so thanks!
Edit 2: Here's some compiling example code with probably bogus semantics:
module Unify (unify) where
import Control.Applicative
import Control.Monad.Trans.State.Strict
data Expr
= Var String -- meta, free an bound vars
| Let String Expr Expr
-- ... more cases
-- no Eq instance, fwiw
-- | If the two terms unify, return the most general unifier, e.g.
-- a substitution (`Map`) of meta variables for terms as association
-- list.
unify :: [String] -> Expr -> Expr -> Maybe [(String, Expr)]
unify metaVars l r = execStateT (go [] [] l r) [] -- threads the current substitution as state
where
go locals floats (Var x) (Var y)
| x == y = return ()
go locals floats (Var x) (Var y)
| lookup x locals == Just y = return ()
go locals floats (Var x) e
| x `elem` metaVars = tryAddSubstitution locals floats x e
go locals floats e (Var y)
| y `elem` metaVars = tryAddSubstitution locals floats y e
-- case in point:
go locals floats (Let x lrhs lbody) (Let y rrhs rbody) = do
go locals floats lrhs rrhs -- try this one, fail current pattern branch if rhss don't unify
-- if we get past the last statement, commit to this branch, no matter
-- the next statement fails or not
go ((x,y):locals) floats lbody rbody
-- try to float the let binding. terms mentioning a floated var might still
-- unify with a meta var
go locals floats (Let x rhs body) e = do
go locals (Left (x,rhs):floats) body e
go locals floats e (Let y rhs body) = do
go locals (Right (y,rhs):floats) body e
go _ _ _ _ = empty
tryAddSubstitution = undefined -- magic
When I need something like this, I just use asum with the blocks inlined. Here I also condensed the multiple patterns Just n1 <- pure a; Just n2 <- pure b into one, (Just n1, Just n2) <- pure (a, b).
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f a b = asum
[ do
(Just n1, Just n2) <- pure (a, b)
m <- compute (n1 + n2)
guard (m == 42)
, do
Just n <- pure a
m <- compute n
guard (m == 42)
, do
Just n <- pure b
m <- compute n
guard (m == 42)
]
You can also use chains of <|>, if you prefer:
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f a b
= do
(Just n1, Just n2) <- pure (a, b)
m <- compute (n1 + n2)
guard (m == 42)
<|> do
Just n <- pure a
m <- compute n
guard (m == 42)
<|> do
Just n <- pure b
m <- compute n
guard (m == 42)
This is about as minimal as you can get for this kind of “fallthrough”.
If you were using Maybe alone, you would be able to do this with pattern guards:
import Control.Monad
import Control.Applicative
ensure :: Alternative f => (a -> Bool) -> a -> f a
ensure p a = a <$ guard (p a)
compute :: Int -> Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> Maybe Int
f (Just m) (Just n)
| Just x <- ensure (== 42) =<< compute (m + n)
= return x
f (Just m) _
| Just x <- ensure (== 42) =<< compute m
= return x
f _ (Just n)
| Just x <- ensure (== 42) =<< compute n
= return x
f _ _ = empty
(ensure is a general purpose combinator. Cf. Lift to Maybe using a predicate)
As you have StateT on the top, though, you would have to supply a state in order to pattern match on Maybe, which would foul up everything. That being so, you are probably better off with something in the vein of your "ugly" solution. Here is a whimsical attempt at improving its looks:
import Control.Monad
import Control.Applicative
import Control.Monad.State
import Control.Monad.Trans
import Data.Foldable
ensure :: Alternative f => (a -> Bool) -> a -> f a
ensure p a = a <$ guard (p a)
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f a b = asum (map (\c -> f' (c a b)) [liftA2 (+), const, flip const])
where
f' = ensure (== 42) <=< compute <=< lift
While this is an answer specific to the snippet I've given, the refactorings only apply limited to the code I was facing.
Perhaps it's not that far-fetched of an idea to extract the skeleton of the asum expression above to a more general combinator:
-- A better name would be welcome.
selector :: Alternative f => (a -> a -> a) -> (a -> f b) -> a -> a -> f b
selector g k x y = asum (fmap (\sel -> k (sel x y)) [g, const, flip const])
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f = selector (liftA2 (+)) (ensure (== 42) <=< compute <=< lift)
Though it is perhaps a bit awkward of a combinator, selector does show the approach is more general than it might appear at first: the only significant restriction is that k has to produce results in some Alternative context.
P.S.: While writing selector with (<|>) instead of asum is arguably more tasteful...
selector g k x y = k (g x y) <|> k x <|> k y
... the asum version straightforwardly generalises to an arbitrary number of pseudo-patterns:
selector :: Alternative f => [a -> a -> a] -> (a -> f b) -> a -> a -> f b
selector gs k x y = asum (fmap (\g -> k (g x y)) gs)
It looks like you could get rid of the whole pattern match by relying on the fact that Int forms a Monoid with addition and 0 as the identity element, and that Maybe a forms a Monoid if a does. Then your function becomes:
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f a b = pure $ a <> b >>= compute >>= pure . mfilter (== 42)
You could generalise by passing the predicate as an argument:
f :: Monoid a => (a -> Bool) -> Maybe a -> Maybe a -> StateT () Maybe a
f p a b = pure $ a <> b >>= compute >>= pure . mfilter p
The only thing is that compute is now taking a Maybe Int as input, but that is just a matter of calling traverse inside that function with whatever computation you need to do.
Edit: Taking into account your last edit, I find that if you spread your pattern matches into separate computations that may fail, then you can just write
f a b = f1 a b <|> f2 a b <|> f3 a b
where f1 (Just a) (Just b) = compute (a + b) >>= check
f1 _ _ = empty
f2 (Just a) _ = compute a >>= check
f2 _ _ = empty
f3 _ (Just b) = compute b >>= check
f3 _ _ = empty
check x = guard (x == 42)
I'm trying to write a function isums that reads n numbers from user and returns their sum. Also, after each number, the sum up to that number is printed. I have this so far:
isums :: Int -> IO Int
isums n = do
num <- readLn
putStrLn (show (num + sum))
sum <- isums (n - 1)
return (num + sum)
Also I'm not using IORef.
This would probably be easiest to express using a helper function, as the extra requirement of printing the partial sums after each input adds a bit of extra clutter:
isums :: Int -> IO Int
isums n = helper n 0
where
helper 0 acc = return acc
helper m acc = do
x <- readLn
let x' = acc + x
print x'
helper (m - 1) x'
What you're doing is kinda like a fold (look at foldM), only instead of traversing a list, you're getting the values "to be folded" from IO. If we had a function:
accM :: Monad m => (a -> m a) -> m a -> Int -> m a
accM f acc 0 = acc
accM f acc n = accM f (acc >>= f) (n - 1)
Then we could write this as:
isums :: Int -> IO Int
isums n = accM helper (return 0) n
where
helper acc = do
x <- readLn
let x' = acc + x
print x'
return x'
Which is a bit nicer (and more reusable) as it lets us separate the general behaviour (accM) from the specific behaviour (helper).
Here is one more solution: it builds on the last version of the #Willem's one, but instead of using a list of ()'s (which is a bit underwhelming) as a fuel for the loop (foldM) it applies a list of actions for reading the values.
import Control.Monad
isums n = foldM go 0 (replicate n readLn)
where
go s a = do
x <- a
let s' = s + x
print s'
return s'
Here replicate n readLn creates a list of actions, each of which reads an integer. These actions are not evaluated till the go is called during the looping by means of foldM. The fact that we can create such a list without performing actual reading stems from the Haskell's laziness.
You can use replicateM :: Applicative m => Int -> m a -> m [a] for this:
import Control.Monad(replicateM)
isums :: (Read n, Num n) => Int -> IO n
isums n = do
numbers <- replicateM n readLn
return (sum numbers)
So here we repeat readLn the given number of times, and then we return the sum of the numbers list.
An equivalent can be achieved with an fmap:
import Control.Monad(replicateM)
isums :: (Read n, Num n) => Int -> IO n
isums n = fmap sum (replicateM n readLn)
Or even pointfree (and pointless):
import Control.Monad(replicateM)
isums :: (Read n, Num n) => Int -> IO n
isums = fmap sum . flip replicateM readLn
We can also produce a list of partial sums with scanl:
import Control.Monad(replicateM)
isums :: (Read n, Num n) => Int -> IO [n]
isums = fmap (scanl (+) 0) . flip replicateM readLn
and then later process the list, or in case we need to print these, and return the last one, we can perform a mapM on that list, like:
import Control.Monad(replicateM)
isums :: (Read n, Num n) => Int -> IO ()
isums n = fmap (scanl (+) 0) (replicateM n readLn) >>= mapM_ print
Or in case we need to print the partial sums:
isums :: (Read n, Num n, Show n) => Int -> IO n
isums n = foldM f 0 (replicate n ())
where f a _ = readLn >>= \b -> let c = a + b in print c >> return c
I'm trying to write a function with StateT only to learn more about it.
In f, I'd like to access to the Int in the last type argument of StateT [Int] IO Int:
f :: StateT [Int] IO Int
f = state $ \xs -> update (error "I want a") xs
update :: Int -> [Int] -> (Int, [Int])
update x [] = (x, [])
update x (y:ys) = (x+y, ys)
Here's how I'd like to call it:
let x = return 55 :: StateT [Int] IO Int
Referencing runStateT:
*Main> :t runStateT
runStateT :: StateT s m a -> s -> m (a, s)
I'd expect to run it:
runStateT (f x) [1,2,3]
to get the following from GHCI, i.e. the IO (Int, [Int]) gets printed:
(56, [2,3])
since the inner a, i.e. 55, + 1, i.e. from [1,2,3], returns (56, [2,3]).
How can I write the above function, getting access to the a?
Ok, here's what say you want:
>>> let x = return 55 :: StateT [Int] IO Int
>>> runStateT (f x) [1,2,3]
(56, [2,3])
So let's work backwards from that.
From the use of f, we can infer its type -
f :: StateT [Int] IO Int -> StateT [Int] IO Int
Note the difference from your given type for f in the question - namely f is a function between values of type StateT [Int] IO Int, not a value of that type.
To define f, we need (>>=) :: Monad m => m a -> (a -> m b) -> m b. This will allow us to take our input of type StateT [Int] IO Int and run some computation on the Int the input computes.
f x = x >>= \i -> state (splitAt 1) >>= \[j] -> return (i + j)
or, using do-notation:
f x = do
i <- x
[j] <- state (splitAt 1)
return (i + j)
Which gives us exactly the result we want.
While this works, it's highly non-idiomatic. Rather than passing monadic values in as inputs to functions and binding them inside the function, it's far more common to define functions that take regular values and return monadic ones, using the bind operator (>>=) outside.
So it'd be far more normal to define
shiftAdd :: Int -> StateT [Int] IO Int
shiftAdd i = do
[j] <- state (splitAt 1)
return (i + j)
So now we can run not only
>>> runStateT (shiftAdd 55) [1,2,3]
(56,[2,3])
but also
>>> runStateT (shiftAdd 55 >>= shiftAdd >>= shiftAdd)
(61,[])
It's still not as idiomatic as it could be as:
I made it unnecessarily partial by using splitAt (it'll throw an exception if the state list is empty)
it's unnecessarily specific (doesn't use IO at all, but we can't use it with other base monads)
Fixing that up gives us:
shiftAdd' :: (Monad m, Num a) => a -> StateT [a] m a
shiftAdd' i = state $ \js -> case js of
[] -> (i, [])
j : js -> (i + j, js)
Which works just fine:
>>> runStateT (return 55 >>= shiftAdd') [1,2,3]
(56,[2,3])
>>> runStateT (return 55 >>= shiftAdd' >>= shiftAdd' >>= shiftAdd') [1,2,3]
(61,[])
>>> runStateT (return 55 >>= shiftAdd' >>= shiftAdd' >>= shiftAdd') []
(55,[])
I would like to generate random sequences from a Markov chain. To generate the Markov chain I use the following code.
module Main where
import qualified Control.Monad.Random as R
import qualified Data.List as L
import qualified Data.Map as M
type TransitionMap = M.Map (String, String) Int
type MarkovChain = M.Map String [(String, Int)]
addTransition :: (String, String) -> TransitionMap -> TransitionMap
addTransition k = M.insertWith (+) k 1
fromTransitionMap :: TransitionMap -> MarkovChain
fromTransitionMap m =
M.fromList [(k, frequencies k) | k <- ks]
where ks = L.nub $ map fst $ M.keys m
frequencies a = map reduce $ filter (outboundFor a) $ M.toList m
outboundFor a k = fst (fst k) == a
reduce e = (snd (fst e), snd e)
After collecting the statistics and generating a Markov Chain object I would like to generate random sequences. I could imagine this method could look something like that (pseudo-code)
generateSequence mc s
| s == "." = s
| otherwise = s ++ " " ++ generateSequence mc s'
where s' = drawRandomlyFrom $ R.fromList $ mc ! s
I would greatly appreciate if someone could explain to me, how I should implement this function.
Edit
If anyone's interested it wasn't as difficult as I thought.
module Main where
import qualified Control.Monad.Random as R
import qualified Data.List as L
import qualified Data.Map as M
type TransitionMap = M.Map (String, String) Rational
type MarkovChain = M.Map String [(String, Rational)]
addTransition :: TransitionMap -> (String, String) -> TransitionMap
addTransition m k = M.insertWith (+) k 1 m
fromTransitionMap :: TransitionMap -> MarkovChain
fromTransitionMap m =
M.fromList [(k, frequencies k) | k <- ks]
where ks = L.nub $ map fst $ M.keys m
frequencies a = map reduce $ filter (outboundFor a) $ M.toList m
outboundFor a k = fst (fst k) == a
reduce e = (snd (fst e), snd e)
generateSequence :: (R.MonadRandom m) => MarkovChain -> String -> m String
generateSequence m s
| not (null s) && last s == '.' = return s
| otherwise = do
s' <- R.fromList $ m M.! s
ss <- generateSequence m s'
return $ if null s then ss else s ++ " " ++ ss
fromSample :: [String] -> MarkovChain
fromSample ss = fromTransitionMap $ foldl addTransition M.empty $ concatMap pairs ss
where pairs s = let ws = words s in zipWith (,) ("":ws) ws
sample :: [String]
sample = [ "I am a monster."
, "I am a rock star."
, "I want to go to Hawaii."
, "I want to eat a hamburger."
, "I have a really big headache."
, "Haskell is a fun language."
, "Go eat a big hamburger."
, "Markov chains are fun to use."
]
main = do
s <- generateSequence (fromSample sample) ""
print s
The only tiny annoyance is the fake "" starting node.
Not sure if this is what you're looking for. This compiles though:
generateSequence :: (R.MonadRandom m) => MarkovChain -> String -> m String
generateSequence mc s | s == "." = return s
| otherwise = do
s' <- R.fromList $ rationalize (mc M.! s)
s'' <- generateSequence mc s'
return $ s ++ " " ++ s''
rationalize :: [(String,Int)] -> [(String,Rational)]
rationalize = map (\(x,i) -> (x, toRational i))
All random number generation needs to happen in either the Random monad or the IO monad. For your purpose, it's probably easiest to understand how to do that in the IO monad, using evalRandIO. In the example below, getRandom is the function we want to use. Now getRandom operates in the Random monad, but we can use evalRandIO to lift it to the IO monad, like this:
main :: IO ()
main = do
x <- evalRandIO getRandom :: IO Double
putStrLn $ "Your random number is " ++ show x
Note: The reason we have to add the type signature to the line that binds x is because in this particular example there are no other hints to tell the compiler what type we want x to be. However, if we used x in some way that makes it clear that we want it to be a Double (e.g., multiplying by another Double), then the type signature wouldn't be necessary.
Using your MarkovChain type, for a current state you can trivially get the available transitions in the form [(nextState,probability)]. (I'm using the word "probability" loosely, it doesn't need to be a true probability; any numeric weight is fine). This is what fromList in Control.Monad.Random is designed for. Again, it operates in the Random monad, but we can use evalRandIO to lift it to the IO monad. Suppose transitions is your list of transitions, having the type [(nextState,probability)]. Then, in the IO monad you can call:
nextState <- evalRandIO $ fromList transitions
You might instead want to create your own function that operates in the Random monad, like this:
getRandomTransition :: RandomGen g => MarkovChain -> String -> Rand g String
getRandomTransition currState chain = do
let transitions = lookup currState chain
fromList transitions
Then you can call this function in the IO monad using evalRandIO, e.g.
nextState <- evalRandIO $ getRandomTransition chain