I am writing a program that runs as a daemon.
To create the daemon, the user supplies a set of
implementations for each of the required classes (one of them is a database)
All of these classes have functions have
type signatures of the form StateT s IO a,
but s is different for each class.
Suppose each of the classes follows this pattern:
import Control.Monad (liftM)
import Control.Monad.State (StateT(..), get)
class Hammer h where
driveNail :: StateT h IO ()
data ClawHammer = MkClawHammer Int -- the real implementation is more complex
instance Hammer ClawHammer where
driveNail = return () -- the real implementation is more complex
-- Plus additional classes for wrenches, screwdrivers, etc.
Now I can define a record that represents the implementation chosen by
the user for each "slot".
data MultiTool h = MultiTool {
hammer :: h
-- Plus additional fields for wrenches, screwdrivers, etc.
}
And the daemon does most of its work in the StateT (MultiTool h ...) IO ()
monad.
Now, since the multitool contains a hammer, I can use it in any situation
where a hammer is needed. In other words, the MultiTool type
can implement any of the classes it contains, if I write code like this:
stateMap :: Monad m => (s -> t) -> (t -> s) -> StateT s m a -> StateT t m a
stateMap f g (StateT h) = StateT $ liftM (fmap f) . h . g
withHammer :: StateT h IO () -> StateT (MultiTool h) IO ()
withHammer runProgram = do
t <- get
stateMap (\h -> t {hammer=h}) hammer runProgram
instance Hammer h => Hammer (MultiTool h) where
driveNail = withHammer driveNail
But the implementations of withHammer, withWrench, withScrewdriver, etc.
are basically identical. It would be nice to be able to write something
like this...
--withMember accessor runProgram = do
-- u <- get
-- stateMap (\h -> u {accessor=h}) accessor runProgram
-- instance Hammer h => Hammer (MultiTool h) where
-- driveNail = withMember hammer driveNail
But of course that won't compile.
I suspect my solution is too object-oriented.
Is there a better way?
Monad transformers, maybe?
Thank you in advance for any suggestions.
If you want to go with a large global state like in your case, then what you want to use is lenses, as suggested by Ben. I too recommend Edward Kmett's lens library. However, there is another, perhaps nicer way.
Servers have the property that the program runs continuously and performs the same operation over a state space. The trouble starts when you want to modularize your server, in which case you want more than just some global state. You want modules to have their own state.
Let's think of a module as something that transforms a Request to a Response:
Module :: (Request -> m Response) -> Module m
Now if it has some state, then this state becomes noticable in that the module might give a different answer the next time. There are a number of ways to do this, for example the following:
Module :: s -> ((Request, s) -> m (Response s)) -> Module m
But a much nicer and equivalent way to express this is the following constructor (we will build a type around it soon):
Module :: (Request -> m (Response, Module m)) -> Module m
This module maps a request to a response, but along the way also returns a new version of itself. Let's go further and make requests and responses polymorphic:
Module :: (a -> m (b, Module m a b)) -> Module m a b
Now if the output type of a module matches another module's input type, then you can compose them like regular functions. This composition is associative and has a polymorphic identity. This sounds a lot like a category, and in fact it is! It is a category, an applicative functor and an arrow.
newtype Module m a b =
Module (a -> m (b, Module m a b))
instance (Monad m) => Applicative (Module m a)
instance (Monad m) => Arrow (Module m)
instance (Monad m) => Category (Module m)
instance (Monad m) => Functor (Module m a)
We can now compose two modules that have their own individual local state without even knowing about it! But that's not sufficient. We want more. How about modules that can be switched among? Let's extend our little module system such that modules can actually choose not to give an answer:
newtype Module m a b =
Module (a -> m (Maybe b, Module m a b))
This allows another form of composition that is orthogonal to (.): Now our type is also a family of Alternative functors:
instance (Monad m) => Alternative (Module m a)
Now a module can choose whether to respond to a request, and if not, the next module will be tried. Simple. You have just reinvented the wire category. =)
Of course you don't need to reinvent this. The Netwire library implements this design pattern and comes with a large library of predefined "modules" (called wires). See the Control.Wire module for a tutorial.
Here's a concrete example of how to use lens like everybody else is talking about. In the following code example, Type1 is the local state (i.e. your hammer), and Type2 is the global state (i.e. your multitool). lens provides the zoom function which lets you run a localized state computation that zooms in on any field defined by a lens:
import Control.Lens
import Control.Monad.Trans.Class (lift)
import Control.Monad.Trans.State
data Type1 = Type1 {
_field1 :: Int ,
_field2 :: Double}
field1 :: SimpleLens Type1 Int
field1 = lens _field1 (\x a -> x { _field1 = a})
field2 :: SimpleLens Type1 Double
field2 = lens _field2 (\x a -> x { _field2 = a})
data Type2 = Type2 {
_type1 :: Type1 ,
_field3 :: String}
type1 :: SimpleLens Type2 Type1
type1 = lens _type1 (\x a -> x { _type1 = a})
field3 :: SimpleLens Type2 String
field3 = lens _field3 (\x a -> x { _field3 = a})
localCode :: StateT Type1 IO ()
localCode = do
field1 += 3
field2 .= 5.0
lift $ putStrLn "Done!"
globalCode :: StateT Type2 IO ()
globalCode = do
f1 <- zoom type1 $ do
localCode
use field1
field3 %= (++ show f1)
f3 <- use field3
lift $ putStrLn f3
main = runStateT globalCode (Type2 (Type1 9 4.0) "Hello: ")
zoom is not limited to immediate sub-fields of a type. Since lenses are composable, you can zoom as deep as you want in a single operation just by doing something like:
zoom (field1a . field2c . field3b . field4j) $ do ...
This sounds very much like an application of lenses.
Lenses are a specification of a sub-field of some data. The idea is you have some value toolLens and functions view and set so that view toolLens :: MultiTool h -> h fetches the tool and set toolLens :: MultiTool h -> h -> MultiTool h replaces it with a new value. Then you can easily define your withMember as a function just accepting a lens.
Lens technology has advanced a great deal recently, and they are now incredibly capable. The most powerful library around at the time of writing is Edward Kmett's lens library, which is a bit much to swallow, but pretty simple once you find the features you want. You can also search for more questions about lenses here on SO, e.g. Functional lenses which links to lenses, fclabels, data-accessor - which library for structure access and mutation is better, or the lenses tag.
I created a lensed extensible record library called data-diverse-lens which allows combining multiple ReaderT (or StateT) like this gist:
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE TypeApplications #-}
module Main where
import Control.Lens
import Control.Monad.Reader
import Control.Monad.State
import Data.Diverse.Lens
import Data.Semigroup
foo :: (MonadReader r m, HasItem' Int r, HasItem' String r) => m (Int, String)
foo = do
i <- view (item' #Int) -- explicitly specify type
s <- view item' -- type can also be inferred
pure (i + 10, s <> "bar")
bar :: (MonadState s m, HasItem' Int s, HasItem' String s) => m ()
bar = do
(item' #Int) %= (+10) -- explicitly specify type
item' %= (<> "bar") -- type can also be inferred
pure ()
main :: IO ()
main = do
-- example of running ReaderT with multiple items
(i, s) <- runReaderT foo ((2 :: Int) ./ "foo" ./ nil)
putStrLn $ show i <> s -- prints out "12foobar"
-- example of running StateT with multiple items
is <- execStateT bar ((2 :: Int) ./ "foo" ./ nil)
putStrLn $ show (view (item #Int) is) <> (view (item #String) is) -- prints out "12foobar"
Data.Has is a simpler library that does the same with tuples. Example from the library front page:
{-# LANGUAGE FlexibleContexts #-}
-- in some library code
...
logInAnyReaderHasLogger :: (Has Logger r, MonadReader r m) => LogString -> m ()
logInAnyReaderHasLogger s = asks getter >>= logWithLogger s
queryInAnyReaderHasSQL :: (Has SqlBackEnd r, MonadReader r m) => Query -> m a
queryInAnyReaderHasSQL q = asks getter >>= queryWithSQL q
...
-- now you want to use these effects together
...
logger <- initLogger ...
sql <- initSqlBackEnd ...
(`runReader` (logger, sql)) $ do
...
logInAnyReaderHasLogger ...
...
x <- queryInAnyReaderHasSQL ...
...
Related
I'd like to write a function
step :: State S O
where O is a record type:
data O = MkO{ out1 :: Int, out2 :: Maybe Int, out3 :: Maybe Bool }
The catch is that I'd like to assemble my O output piecewise. What I mean by that, is that at various places along the definition of step, I learn then and there that e.g. out2 should be Just 3, but I don't know in a non-convoluted way what out1 and out3 should be. Also, there is a natural default value for out1 that can be computed from the end state; but there still needs to be the possibility to override it in step.
And, most importantly, I want to "librarize" this, so that users can provide their own S and O types, and I give them the rest.
My current approach is to wrap everything in a WriterT (HKD O Last) using Higgledy's automated way of creating a type HKD O Last which is isomorphic to
data OLast = MkOLast{ out1' :: Last Int, out2' :: Last (Maybe Int), out3' :: Last (Maybe String) }
This comes with the obvious Monoid instance, so I can, at least morally, do the following:
step = do
MkOLast{..} <- execWriterT step'
s <- get
return O
{ out1 = fromMaybe (defaultOut1 s) $ getLast out1'
, out2 = getLast out2'
, out3 = fromMaybe False $ getLast out3'
}
step' = do
...
tell mempty{ out2' = pure $ Just 42 }
...
tell mempty{ out1' = pure 3 }
This is code I could live with.
The problem is that I can only do this morally. In practice, what I have to write is quite convoluted code because Higgledy's HKD O Last exposes record fields as lenses, so the real code ends up looking more like the following:
step = do
oLast <- execWriterT step'
s <- get
let def = defaultOut s
return $ runIdentity . construct $ bzipWith (\i -> maybe i Identity . getLast) (deconstruct def) oLast
step' = do
...
tell $ set (field #"out2") (pure $ Just 42) mempty
...
tell $ set (field #"out3") (pure 3) mempty
The first wart in step we can hide away behind a function:
update :: (Generic a, Construct Identity a, FunctorB (HKD a), ProductBC (HKD a)) => a -> HKD a Last -> a
update initial edits = runIdentity . construct $ bzipWith (\i -> maybe i Identity . getLast) (deconstruct initial) edits
so we can "librarize" that as
runStep
:: (Generic o, Construct Identity o, FunctorB (HKD o), ProductBC (HKD o))
=> (s -> o) -> WriterT (HKD o Last) (State s) () -> State s o
runStep mkDef step = do
let updates = execWriterT step s
def <- gets mkDef
return $ update def updates
But what worries me are the places where partial outputs are recorded. So far, the best I've been able to come up with is to use OverloadedLabels to provide #out2 as a possible syntax:
instance (HasField' field (HKD a f) (f b), Applicative f) => IsLabel field (b -> Endo (HKD a f)) where
fromLabel x = Endo $ field #field .~ pure x
output :: (Monoid (HKD o Last)) => Endo (HKD o Last) -> WriterT (HKD o Last) (State s) ()
output f = tell $ appEndo f mempty
this allows end-users to write step' as
step' = do
...
output $ #out2 (Just 42)
...
output $ #out3 3
but it's still a bit cumbersome; moreover, it uses quite a lot of heavy machinery behind the scenes. Especially given that my use case is such that all the library internals would need to be explained step-by-step.
So, what I am looking for are improvements in the following areas:
Simpler internal implementation
Nicer API for end-users
I'd be happy with a completely different approach from first principles as well, as long as it doesn't require the user to define their own OLast next to O...
The following is not a very satisfactory solution because it's still complex and the type errors are horrific, but it tries to achieve two things:
Any attempt to "complete" the construction of the record without having specified all mandatory fields results in a type error.
"there is a natural default value for out1 that can be computed from the end state; but there still needs to be the possibility to override it"
The solution does away with the State monad. Instead, there's an extensible record to which new fields are progressively added—therefore changing its type—until it is "complete".
We use the red-black-record, sop-core (these for HKD-like functionality) and transformers (for the Reader monad) packages.
Some necessary imports:
{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE TypeApplications #-}
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE AllowAmbiguousTypes #-}
{-# LANGUAGE PartialTypeSignatures #-}
{-# OPTIONS_GHC -Wno-partial-type-signatures #-}
import Data.RBR (Record,unit,FromRecord(fromRecord),ToRecord,RecordCode,
Productlike,fromNP,toNP,ProductlikeSubset,projectSubset,
FromList,
Insertable,Insert,insert) -- from "red-black-record"
import Data.SOP (I(I),unI,NP,All,Top) -- from "sop-core"
import Data.SOP.NP (sequence_NP)
import Data.Function (fix)
import Control.Monad.Trans.Reader (Reader,runReader,reader)
import qualified GHC.Generics
The datatype-generic machinery:
specify :: forall k v t r. Insertable k v t
=> v -> Record (Reader r) t -> Record (Reader r) (Insert k v t)
specify v = insert #k #v #t (reader (const v))
close :: forall r subset subsetflat whole . _ => Record (Reader r) whole -> r
close = fixRecord #r #subsetflat . projectSubset #subset #whole #subsetflat
where
fixRecord
:: forall r flat. (FromRecord r, Productlike '[] (RecordCode r) flat, All Top flat)
=> Record (Reader r) (RecordCode r)
-> r
fixRecord = unI . fixHelper I
fixHelper
:: forall r flat f g. _
=> (NP f flat -> g (NP (Reader r) flat))
-> Record f (RecordCode r)
-> g r
fixHelper adapt r = do
let moveFunctionOutside np = runReader . sequence_NP $ np
record2record np = fromRecord . fromNP <$> moveFunctionOutside np
fix . record2record <$> adapt (toNP r)
specify adds a field to an extensible HKD-like record where each field is actually a function from the completed record to the type of the field in the completed record. It inserts the field as a constant function. It can also override existing default fields.
close takes an extensible record constructed with specify and "ties the knot", returning the completed non-HKD record.
Here's code that must be written for each concrete record:
data O = MkO { out1 :: Int, out2 :: Maybe Int, out3 :: Maybe Bool }
deriving (GHC.Generics.Generic, Show)
instance FromRecord O
instance ToRecord O
type ODefaults = FromList '[ '("out1",Int) ]
odefaults :: Record (Reader O) ODefaults
odefaults =
insert #"out1" (reader $ \r -> case out2 r of
Just i -> succ i
Nothing -> 0)
$ unit
In odefaults we specify overrideable default values for some fields, which are calculated by inspecting the "completed" record (this works because we later tie the knot with close.)
Putting it all to work:
example1 :: O
example1 =
close
. specify #"out3" (Just False)
. specify #"out2" (Just 0)
$ odefaults
example2override :: O
example2override =
close
. specify #"out1" (12 :: Int)
. specify #"out3" (Just False)
. specify #"out2" (Just 0)
$ odefaults
main :: IO ()
main =
do print $ example1
print $ example2override
-- result:
-- MkO {out1 = 1, out2 = Just 0, out3 = Just False}
-- MkO {out1 = 12, out2 = Just 0, out3 = Just False}
Here's what I am currently using for this: basically the same Barbies-based technique from my original question, but using barbies-th and lens to create properly named field lenses.
I am going to illustrate it with an example. Suppose I want to collect this result:
data CPUOut = CPUOut
{ inputNeeded :: Bool
, ...
}
Create Barbie for CPUOut using barbies-th, add _ prefix to field names, and use lens's makeLenses TH macro to generate field accessors:
declareBareB [d|
data CPUOut = CPUOut
{ _inputNeeded :: Bool
, ...
} |]
makeLenses ''CPUState
Write update s.t. it works on partial values that are wrapped in the Barbie newtype wrapper:
type Raw b = b Bare Identity
type Partial b = Barbie (b Covered) Last
update
:: (BareB b, ApplicativeB (b Covered))
=> Raw b -> Partial b -> Raw b
update initials edits =
bstrip $ bzipWith update1 (bcover initials) (getBarbie edits)
where
update1 :: Identity a -> Last a -> Identity a
update1 initial edit = maybe initial Identity (getLast edit)
The role of the Barbie wrapper is that Barbie b f has a Monoid instance if only all the fields of b f are monoids themselves. This is exactly the case for Partial CPUOut, so that is what we are going to be collecting in our WriterT:
type CPU = WriterT (Partial CPUOut) (State CPUState)
Write the generic output assignment combinator. This is what makes it nicer than the approach in the original question, because the Setter's are properly named field accessor lenses, not overloaded labels:
(.:=)
:: (Applicative f, MonadWriter (Barbie b f) m)
=> Setter' (b f) (f a) -> a -> m ()
fd .:= x = scribe (iso getBarbie Barbie . fd) (pure x)
Example use:
startInput :: CPU ()
startInput = do
inputNeeded .:= True
phase .= WaitInput
I have several State monad actions. Some of the actions make decisions based on the current state and other input optionally generating result. The two types of actions invoke each other.
I have modeled these two action types with State and StateT Maybe. The following (contrived) example shows my current approach.
{-# LANGUAGE MultiWayIf #-}
import Control.Monad (guard)
import Control.Monad.Identity (runIdentity)
import Control.Monad.Trans.State
type Producer = Int -> State [Int] Int
type MaybeProducer = Int -> StateT [Int] Maybe Int
produce :: Producer
produce n
| n <= 0 = return 0
| otherwise = do accum <- get
let mRes = runStateT (maybeProduce n) accum
if | Just res <- mRes -> StateT $ const (return res)
| otherwise -> do res <- produce (n - 1)
return $ res + n
maybeProduce :: MaybeProducer
maybeProduce n = do guard $ odd n
modify (n:)
mapStateT (return . runIdentity) $
do res <- produce (n - 1)
return $ res + n
I have factored out separating the checks from the actions (thus transforming them into simple State actions) because the check itself is very involved (80% of the work) and provides the bindings needed in the action. I don't want to promote the State actions to StateT Maybe either, because it poses an inaccurate model.
Is there a better or more elegan way that I'm missing? In particular I don't like the mapStateT/runStateT duo, but it seems necessary.
PS: I know the example is actually a Writer, but I used State to better reflect the real case
I don't want to promote the State actions to StateT Maybe either, because it poses an inaccurate model.
What do you mean by "promote"? I can't tell which of these you mean:
Rewrite the definitions of the State actions so that their type is now StateT Maybe, even though they don't rely on Maybe at all;
Use an adapter function that transforms State s a into StateT s Maybe a.
I agree with rejecting (1), but to me that mean either:
Go for (2). One useful tool for this is to use the mmorph library (blog entry).
Rewrite the actions from State s a to use Monad m => StateT s m a.
In the second case, the type is compatible with any Monad m but does not allow the code to assume any specific base monad, so you get the same purity as State s a.
I'd give mmorph a shot. Note that:
State s a = StateT s Identity a;
hoist generalize :: (MFunctor t, Monad m) => t Identity a -> t m a;
And that specializes to hoist generalize :: State s a -> StateT s Maybe a.
EDIT: It's worth nothing that there is an isomorphism between the State s a and forall m. StateT s m a types, given by these inverse functions:
{-# LANGUAGE RankNTypes #-}
import Control.Monad.Morph
import Control.Monad.Trans
import Control.Monad.Trans.State
import Control.Monad.Identity
fwd :: (MFunctor t, Monad m) => t Identity a -> t m a
fwd = hoist generalize
-- The `forall` in the signature forbids callers from demanding any
-- specific choice of type for `m`, which allows *us* to choose
-- `Identity` for `m` here.
bck :: MFunctor t => (forall m. t m a) -> t Identity a
bck = hoist generalize
So the Monad m => StateT s m a and mmorph solutions are, effectively, the same. I prefer using mmorph here, though.
I often end up in a situation where it's very convenient to be using the State monad, due to having a lot of related functions that need to operate on the same piece of data in a semi-imperative way.
Some of the functions need to read the data in the State monad, but will never need to change it. Using the State monad as usual in these functions works just fine, but I can't help but feel that I've given up Haskell's inherent safety and replicated a language where any function can mutate anything.
Is there some type-level thing that I can do to ensure that these functions can only read from the State, and never write to it?
Current situation:
iWriteData :: Int -> State MyState ()
iWriteData n = do
state <- get
put (doSomething n state)
-- Ideally this type would show that the state can't change.
iReadData :: State MyState Int
iReadData = do
state <- get
return (getPieceOf state)
bigFunction :: State MyState ()
bigFunction = do
iWriteData 5
iWriteData 10
num <- iReadData -- How do we know that the state wasn't modified?
iWRiteData num
Ideally iReadData would probably have the type Reader MyState Int, but then it doesn't play nicely with the State. Having iReadData be a regular function seems to be the best bet, but then I have to go through the gymnastics of explicitly extracting and passing it the state every time it's used. What are my options?
It's not hard to inject the Reader monad into State:
read :: Reader s a -> State s a
read a = gets (runReader a)
then you could say
iReadData :: Reader MyState Int
iReadData = do
state <- ask
return (getPieceOf state)
and call it as
x <- read $ iReadData
this would allow you to build up Readers into larger read-only sub-programs and inject them into State only where you need to combine them with mutators.
It's not hard to extend this to a ReaderT and StateT at the top of your monad transformer stack (in fact, the definition above works exactly for this case, just change the type). Extending it to a ReaderT and StateT in the middle of the stack is harder. You basically need a function
lift1 :: (forall a. m0 a -> m1 a) -> t m0 a -> t m1 a
for every monad transformer t in the stack above the ReaderT/StateT, which isn't part of the standard library.
I would recommend wrapping up the State monad in a newtype and defining a MonadReader instance for it:
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
{-# LANGUAGE MultiParamTypeClasses #-}
{-# LANGUAGE FlexibleContexts #-}
import Control.Applicative
import Control.Monad.State
import Control.Monad.Reader
data MyState = MyState Int deriving Show
newtype App a = App
{ runApp' :: State MyState a
} deriving
( Functor
, Applicative
, Monad
, MonadState MyState
)
runApp :: App a -> MyState -> (a, MyState)
runApp app = runState $ runApp' app
instance MonadReader MyState App where
ask = get
local f m = App $ fmap (fst . runApp m . f) $ get
iWriteData :: MonadState MyState m => Int -> m ()
iWriteData n = do
MyState s <- get
put $ MyState $ s + n
iReadData :: MonadReader MyState m => m Int
iReadData = do
MyState s <- ask
return $ s * 2
bigFunction :: App ()
bigFunction = do
iWriteData 5
iWriteData 10
num <- iReadData
iWriteData num
This is certainly more code that #jcast's solution, but it follows the the tradition of implementing your transformer stack as a newtype wrapper, and by sticking with constraints instead of solidified types you can make strong guarantees about the use of your code while providing maximum flexibility for re-use. Anyone using your code would be able to extend your App with transformers of their own while still using iReadData and iWriteData as intended. You also don't have to wrap every call to a Reader monad with a read function, the MonadReader MyState functions are seamlessly integrated with functions in the App monad.
Excellent answers by jcast and bhelkir, with exactly the first idea I thought of—embedding Reader inside State.
I think it's worthwhile to address this semi-side point of your question:
Using the State monad as usual in these functions works just fine, but I can't help but feel that I've given up Haskell's inherent safety and replicated a language where any function can mutate anything.
That's a potential red flag, indeed. I've always found that State works best for code with "small" states that can be contained within the lifetime of a single, brief application of runState. My go-to example is numbering the elements of a Traversable data structure:
import Control.Monad.State
import Data.Traversable (Traversable, traverse)
tag :: (Traversable t, Enum s) => s -> t a -> t (s, a)
tag i ta = evalState (traverse step ta) init
where step a = do s <- postIncrement
return (s, a)
postIncrement :: Enum s => State s s
postIncrement = do result <- get
put (succ result)
return result
You don't directly say so, but you make it sound you may have a big state value, with many different fields being used in many different ways within a long-lived runState call. And perhaps it does need to be that way for your program at this point. But one technique for coping with this might be to write your smaller State actions so that they only use narrower state types than the "big" one and then embed these into the larger State type with a function like this:
-- | Extract a piece of the current state and run an action that reads
-- and modifies only that piece.
substate :: (s -> s') -> (s' -> s -> s) -> State s' a -> State s a
substate extract replace action =
do s <- get
let (s', a) = runState action (extract s)
put (replace s' s)
return a
Schematic example
example :: State (A, B) Whatever
example = do foo <- substate fst (,b) action1
bar <- substate snd (a,) action2
return $ makeWhatever foo bar
-- Can only touch the `A` component of the state
action1 :: State A Foo
action1 = ...
-- Can only touch the `B` component of the state
action2 :: State B Bar
action2 = ...
Note that the extract and replace functions constitute a lens, and there are libraries for that, which may even already include a function like this.
I'm trying to write a two-player game in Haskell, such as checkers. I envision having types GameState, Move, and a function result :: GameState -> Move -> GameState that defines the game rules. I want to have both human and automated players, and I figured I'd do this by having a typeclass:
class Player p m | p -> m where
selectMove :: p -> GameState -> m Move
where the idea would be that m could be Identity for a basic AI player, IO for a human, State for an AI that maintains state across moves, etc. The question is how to go from these to the overall game loop. I figure I could define something like:
Player p1 m1, Player p2 m2 => moveList :: p1 -> p2 -> GameState -> m1 m2 [Move]
a monadic function that takes in the players and initial state, and returns the lazy list of moves. But then on top of this let's say I want a text-based interface that, say, allows first selecting each player from a list of possibilities, then causes the game to be played. So I'd need:
playGame :: IO ()
I can't see how to define playGame given moveList in a generic way. Or is my overall approach not right?
EDIT: thinking further about it, I don't even see how to define moveList above. E.g., if player 1 was a human, so IO, and player 2 was a stateful AI, so State, the first move of player 1 would have type IO Move. Then player 2 would have to take the resulting state of type IO GameState and produce a move of type State IO Move, and player 1's next move would be of type IO State IO Move? That doesn't look right.
There are two parts to this question:
How to mix a monad-independent chess-playing framework with incremental monad-specific input
How to specify the monad-specific part at run time
You solve the former problem using a generator, which is a special case of a free monad transformer:
import Control.Monad.Trans.Free -- from the "free" package
type GeneratorT a m r = FreeT ((,) a) m r
-- or: type Generator a = FreeT ((,) a)
yield :: (Monad m) => a -> GeneratorT a m ()
yield a = liftF (a, ())
GeneratorT a is a monad transformer (because FreeT f is a monad transformer, for free, when f is a Functor). This means we can mix yield (which is polymorphic in the base monad), with monad-specific calls by using lift to invoke the base monad.
I'll define some fake chess moves just for this example:
data ChessMove = EnPassant | Check | CheckMate deriving (Read, Show)
Now, I'll define an IO based generator of chess moves:
import Control.Monad
import Control.Monad.Trans.Class
ioPlayer :: GeneratorT ChessMove IO r
ioPlayer = forever $ do
lift $ putStrLn "Enter a move:"
move <- lift readLn
yield move
That was easy! We can unwrap the result one move at a time using runFreeT, which will only demand the player input a move when you bind the the result:
runIOPlayer :: GeneratorT ChessMove IO r -> IO r
runIOPlayer p = do
x <- runFreeT p -- This is when it requests input from the player
case x of
Pure r -> return r
Free (move, p') -> do
putStrLn "Player entered:"
print move
runIOPlayer p'
Let's test it:
>>> runIOPlayer ioPlayer
Enter a move:
EnPassant
Player entered:
EnPassant
Enter a move:
Check
Player entered:
Check
...
We can do the same thing using the Identity monad as the base monad:
import Data.Functor.Identity
type Free f r = FreeT f Identity r
runFree :: (Functor f) => Free f r -> FreeF f r (Free f r)
runFree = runIdentity . runFreeT
NoteThe transformers-free packages defines these already (Disclaimer: I wrote it and Edward merged its functionality was merged into the free package. I only keep it for teaching purposes and you should use free if possible).
With those in hand, we can define pure chess move generators:
type Generator a r = Free ((,) a) r
-- or type Generator a = Free ((,) a)
purePlayer :: Generator ChessMove ()
purePlayer = do
yield Check
yield CheckMate
purePlayerToList :: Generator ChessMove r -> [ChessMove]
purePlayerToList p = case (runFree p) of
Pure _ -> []
Free (move, p') -> move:purePlayerToList p'
purePlayerToIO :: Generator ChessMove r -> IO r
purePlayerToIO p = case (runFree p) of
Pure r -> return r
Free (move, p') -> do
putStrLn "Player entered: "
print move
purePlayerToIO p'
Let's test it:
>>> purePlayerToList purePlayer
[Check, CheckMate]
Now, to answer your next question, which is how to choose the base monad at run time. This is easy:
main = do
putStrLn "Pick a monad!"
whichMonad <- getLine
case whichMonad of
"IO" -> runIOPlayer ioPlayer
"Pure" -> purePlayerToIO purePlayer
"Purer!" -> print $ purePlayerToList purePlayer
Now, here is where things get tricky. You actually want two players, and you want to specify the base monad for both of them independently. To do this, you need a way to retrieve one move from each player as an action in the IO monad and save the rest of the player's move list for later:
step
:: GeneratorT ChessMove m r
-> IO (Either r (ChessMove, GeneratorT ChessMove m r))
The Either r part is in case the player runs out of moves (i.e. reaches the end of their monad), in which case the r is the block's return value.
This function is specific to each monad m, so we can type class it:
class Step m where
step :: GeneratorT ChessMove m r
-> IO (Either r (ChessMove, GeneratorT ChessMove m r))
Let's define some instances:
instance Step IO where
step p = do
x <- runFreeT p
case x of
Pure r -> return $ Left r
Free (move, p') -> return $ Right (move, p')
instance Step Identity where
step p = case (runFree p) of
Pure r -> return $ Left r
Free (move, p') -> return $ Right (move, p')
Now, we can write our game loop to look like:
gameLoop
:: (Step m1, Step m2)
=> GeneratorT ChessMove m1 a
-> GeneratorT ChessMove m2 b
-> IO ()
gameLoop p1 p2 = do
e1 <- step p1
e2 <- step p2
case (e1, e2) of
(Left r1, _) -> <handle running out of moves>
(_, Left r2) -> <handle running out of moves>
(Right (move1, p2'), Right (move2, p2')) -> do
<do something with move1 and move2>
gameLoop p1' p2'
And our main function just selects which players to use:
main = do
p1 <- getStrLn
p2 <- getStrLn
case (p1, p2) of
("IO", "Pure") -> gameLoop ioPlayer purePlayer
("IO", "IO" ) -> gameLoop ioPlayer ioPlayer
...
I hope that helps. That was probably a bit over kill (and you can probably use something simpler than generators), but I wanted to give a general tour of cool Haskell idioms that you can sample from when designing your game. I type-checked all but the last few code blocks, since I couldn't come up with a sensible game logic to test on the fly.
You can learn more about free monads and free monad transformers if those examples didn't suffice.
My advice has two main parts:
Skip defining a new type class.
Program to the interfaces defined by existing type classes.
For the first part, what I mean is you should consider creating a data type like
data Player m = Player { selectMove :: m Move }
-- or even
type Player m = m Move
What the second part means is to use classes like MonadIO and MonadState to keep your Player values polymorphic, and choose an appropriate monad instance only at the end after combining all the players. For example, you might have
computerPlayer :: MonadReader GameState m => Player m
randomPlayer :: MonadRandom m => Player m
humanPlayer :: (MonadIO m, MonadReader GameState m) => Player m
Perhaps you will find there are other players you want, too. Anyway, the point of this is that once you've created all these players, if they are typeclass polymorphic as above, you may choose a particular monad that implements all the required classes and you are done. For example, for these three, you might choose ReaderT GameState IO.
Good luck!
I'm working on a Haskell project that involves tying a big knot: I'm parsing a serialized representation of a graph, where each node is at some offset into the file, and may reference another node by its offset. So I need to build up a map from offsets to nodes while parsing, which I can feed back to myself in a do rec block.
I have this working, and kinda-sorta-reasonably abstracted into a StateT-esque monad transformer:
{-# LANGUAGE DoRec, GeneralizedNewtypeDeriving #-}
import qualified Control.Monad.State as S
data Knot s = Knot { past :: s, future :: s }
newtype RecStateT s m a = RecStateT (S.StateT (Knot s) m a) deriving
( Alternative
, Applicative
, Functor
, Monad
, MonadCont
, MonadError e
, MonadFix
, MonadIO
, MonadPlus
, MonadReader r
, MonadTrans
, MonadWriter w )
runRecStateT :: RecStateT s m a -> Knot s -> m (a, Knot s)
runRecStateT (RecStateT st) = S.runStateT st
tie :: MonadFix m => RecStateT s m a -> s -> m (a, s)
tie m s = do
rec (a, Knot s' _) <- runRecStateT m (Knot s s')
return (a, s')
get :: Monad m => RecStateT s m (Knot s)
get = RecStateT S.get
put :: Monad m => s -> RecStateT s m ()
put s = RecStateT $ S.modify $ \ ~(Knot _ s') -> Knot s s'
The tie function is where the magic happens: the call to runRecStateT produces a value and a state, which I feed it as its own future. Note that get allows you to read from both the past and future states, but put only allows you to modify the "present."
Question 1: Does this seem like a decent way to implement this knot-tying pattern in general? Or better still, has somebody implemented a general solution to this, that I overlooked when snooping through Hackage? I beat my head against the Cont monad for a while, since it seemed possibly more elegant (see similar post from Dan Burton), but I just couldn't work it out.
Totally subjective Question 2: I'm not totally thrilled with the way my calling code ends up looking:
do
Knot past future <- get
let {- ... -} = past
{- ... -} = future
node = {- ... -}
put $ {- ... -}
return node
Implementation details here omitted, obviously, the important point being that I have to get the past and future state, pattern-match them inside a let binding (or explicitly make the previous pattern lazy) to extract whatever I care about, then build my node, update my state and finally return the node. Seems unnecessarily verbose, and I particularly dislike how easy it is to accidentally make the pattern that extracts the past and future states strict. So, can anybody think of a nicer interface?
I've been playing around with stuff, and I think I've come up with something... interesting. I call it the "Seer" monad, and it provides (aside from Monad operations) two primitive operations:
see :: Monoid s => Seer s s
send :: Monoid s => s -> Seer s ()
and a run operation:
runSeer :: Monoid s => Seer s a -> a
The way this monad works is that see allows a seer to see everything, and send allows a seer to "send" information to all other seers for them to see. Whenever any seer performs the see operation, they are able to see all of the information that has been sent, and all of the information that will be sent. In other words, within a given run, see will always produce the same result no matter where or when you call it. Another way of saying it is that see is how you get a working reference to the "tied" knot.
This is actually very similar to just using fix, except that all of the sub-parts are added incrementally and implicitly, rather than explicitly. Obviously, seers will not work correctly in the presence of a paradox, and sufficient laziness is required. For example, see >>= send may cause an explosion of information, trapping you in a time loop.
A dumb example:
import Control.Seer
import qualified Data.Map as M
import Data.Map (Map, (!))
bar :: Seer (Map Int Char) String
bar = do
m <- see
send (M.singleton 1 $ succ (m ! 2))
send (M.singleton 2 'c')
return [m ! 1, m ! 2]
As I said, I've just been toying around, so I have no idea if this is any better than what you've got, or if it's any good at all! But it's nifty, and relevant, and if your "knot" state is a Monoid, then it just might be useful to you. Fair warning: I built Seer by using a Tardis.
https://github.com/DanBurton/tardis/blob/master/Control/Seer.hs
I wrote up an article on this topic at entitled Assembly: Circular Programming with Recursive do where I describe two methods for building an assembler using knot tying. Like your problem, an assembler has to be able to resolve address of labels that may occur later in the file.
Regarding the implementation, I would make it a composition of a Reader monad (for the future) and a State monad (for past/present). The reason is that you set your future only once (in tie) and then don't change it.
{-# LANGUAGE DoRec, GeneralizedNewtypeDeriving #-}
import Control.Monad.State
import Control.Monad.Reader
import Control.Applicative
newtype RecStateT s m a = RecStateT (StateT s (ReaderT s m) a) deriving
( Alternative
, Applicative
, Functor
, Monad
, MonadPlus
)
tie :: MonadFix m => RecStateT s m a -> s -> m (a, s)
tie (RecStateT m) s = do
rec (a, s') <- flip runReaderT s' $ flip runStateT s m
return (a, s')
getPast :: Monad m => RecStateT s m s
getPast = RecStateT get
getFuture :: Monad m => RecStateT s m s
getFuture = RecStateT ask
putPresent :: Monad m => s -> RecStateT s m ()
putPresent = RecStateT . put
Regarding your second question, it'd help to know your dataflow (i.e. to have a minimal example of your code). It's not true that strict patterns always lead to loops. It's true that you need to be careful so as not to create a non-producing loop, but the exact restrictions depend on what and how you're building.
I had a similar problem recently, but I chose a different approach. A recursive data structure can be represented as a type fixed point on a data type functor. Loading data can be then split into two parts:
Load the data into a structure that references other nodes only by some kind of identifier. In the example it's Loader Int (NodeF Int), which constructs a map of values of type NodeF Int Int.
Tie the knot by creating a recursive data structure by replacing the identifiers with actual data. In the example the resulting data structures have type Fix (NodeF Int), and they are later converted to Node Int for convenience.
It's lacking a proper error handling etc., but the idea should be clear from that.
-- Public Domain
import Control.Monad
import Data.Map (Map)
import qualified Data.Map as Map
import Data.Maybe (fromJust)
-- Fixed point operator on types and catamohism/anamorphism methods
-- for constructing/deconstructing them:
newtype Fix f = Fix { unfix :: f (Fix f) }
catam :: Functor f => (f a -> a) -> (Fix f -> a)
catam f = f . fmap (catam f) . unfix
anam :: Functor f => (a -> f a) -> (a -> Fix f)
anam f = Fix . fmap (anam f) . f
anam' :: Functor f => (a -> f a) -> (f a -> Fix f)
anam' f = Fix . fmap (anam f)
-- The loader itself
-- A representation of a loader. Type parameter 'k' represents the keys by
-- which the nodes are represented. Type parameter 'v' represents a functor
-- data type representing the values.
data Loader k v = Loader (Map k (v k))
-- | Creates an empty loader.
empty :: Loader k v
empty = Loader $ Map.empty
-- | Adds a new node into a loader.
update :: (Ord k) => k -> v k -> Loader k v -> Loader k v
update k v = update' k (const v)
-- | Modifies a node in a loader.
update' :: (Ord k) => k -> (Maybe (v k) -> (v k)) -> Loader k v -> Loader k v
update' k f (Loader m) = Loader $ Map.insertWith (const (f . Just)) k (f Nothing) $ m
-- | Does the actual knot-tying. Creates a new data structure
-- where the references to nodes are replaced by the actual data.
tie :: (Ord k, Functor v) => Loader k v -> Map k (Fix v)
tie (Loader m) = Map.map (anam' $ \k -> fromJust (Map.lookup k m)) m
-- -----------------------------------------------------------------
-- Usage example:
data NodeF n t = NodeF n [t]
instance Functor (NodeF n) where
fmap f (NodeF n xs) = NodeF n (map f xs)
-- A data structure isomorphic to Fix (NodeF n), but easier to work with.
data Node n = Node n [Node n]
deriving Show
-- The isomorphism that does the conversion.
nodeunfix :: Fix (NodeF n) -> Node n
nodeunfix = catam (\(NodeF n ts) -> Node n ts)
main :: IO ()
main = do
-- Each node description consist of an integer ID and a list of other nodes
-- it references.
let lss =
[ (1, [4])
, (2, [1])
, (3, [2, 1])
, (4, [3, 2, 1])
, (5, [5])
]
print lss
-- Fill a new loader with the data:
let
loader = foldr f empty lss
f (label, dependsOn) = update label (NodeF label dependsOn)
-- Tie the knot:
let tied' = tie loader
-- And convert Fix (NodeF n) into Node n:
let tied = Map.map nodeunfix tied'
-- For each node print the label of the first node it references
-- and the count of all referenced nodes.
print $ Map.map (\(Node n ls#((Node n1 _) : _)) -> (n1, length ls)) tied
I'm kind of overwhelmed by the amount of Monad usage.
I might not understand the past/future things, but I guess you are just trying to express the lazy+fixpoint binding. (Correct me if I'm wrong.)
The RWS Monad usage with R=W is kind of funny, but you do not need the State and the loop, when you can do the same with fmap. There is no point in using Monads if they do not make things easier. (Only very few Monads represent chronological order, anyway.)
My general solution to tying the knot:
I parse everything to a List of nodes,
convert that list to a Data.Vector for O(1) access to boxed (=lazy) values,
bind that result to a name using let or the fix or mfix function,
and access that named Vector inside the parser. (see 1.)
That example solution in your blog, where you write sth. like this:
data Node = Node {
value :: Int,
next :: Node
} deriving Show
…
tie = …
parse = …
data ParserState = …
…
example :: Node
example =
let (_, _, m) = tie parse $ ParserState 0 [(0, 1), (1, 2), (2, 0)]
in (m Map.! 0)
I would have written this way:
{-# LANGUAGE ViewPatterns, NamedFieldPuns #-}
import Data.Vector as Vector
example :: Node
example =
let node :: Int -> Node
node = (Vector.!) $ Vector.fromList $
[ Node{value,next}
| (value,node->next) <- [(0, 1), (1, 2), (2, 0)]
]
in (node 0)
or shorter:
{-# LANGUAGE ViewPatterns, NamedFieldPuns #-}
import Data.Vector as Vector
example :: Node
example = (\node->(Vector.fromList[ Node{value,next}
| (value,node->next) <- [(0, 1), (1, 2), (2, 0)]
] Vector.!)) `fix` 0