If you have programmed in any other language before, you likely wrote some functions that "kept state". For those new to the concept, a state is one or more variables that are required to perform some computation but are not among the arguments of the relevant function. Objectoriented languages, like C++, make extensive usage of state variables within objects in the form of member variables. Procedural languages, like C, use variables declared outside the current scope to keep track of state.
In Haskell, however, such techniques cannot be applied in a straightforward way; they require mutable variables, and that clashes with Haskell's functional purity. We can usually keep track of state by passing parameters from function to function or by pattern matching of various sorts, but in some cases it is appropriate to find a more general or convenient solution. We will consider the common example of how the State
monad can assist us in generating pseudorandom numbers.
PseudoRandom NumbersEdit
Generating actual random numbers is a very complicated subject. Computer programming almost always sticks to pseudorandom numbers. They are called "pseudo" because they are not truly random. Starting from an initial state (commonly called the seed), pseudorandom number generators produce a sequence of numbers that have the appearance of being random.
Every time a pseudorandom number is requested, a global state is updated.^{[1]} Sequences of pseudorandom numbers can be replicated exactly if the initial seed and the algorithm is known.
Implementation in HaskellEdit
Producing a pseudorandom number in most programming languages is very simple: there is usually a function, such as C or C++'s rand()
, that provides a pseudorandom value (or a truly random one, depending on the implementation). Haskell has a similar one in the System.Random
module:
> :m System.Random > :t randomIO randomIO :: Random a => IO a > randomIO 1557093684
This function references a mutable state that is held outside Haskell and interacted with via IO
, so values obtained using randomIO
will be different every time.
Example: Rolling DiceEdit
Suppose we are coding a game in which at some point we need an element of chance. In reallife games that is often obtained by means of dice. So, let's create a dicethrowing function in Haskell.
We'll use the function randomR
to specify an interval from which the pseudorandom values will be taken; in the case of a die, it is randomR (1,6)
. To make sure we get new values each time we roll, we'll use the IO
version of randomR
:
import Control.Monad import System.Random rollDiceIO :: IO (Int, Int) rollDiceIO = liftM2 (,) (randomRIO (1,6)) (randomRIO (1,6))
That function rolls two dice. Here, liftM2
is used to make the nonmonadic twoargument function (,)
work within a monad. The (,)
is the noninfix version of the tuple constructor. Thus, the two die rolls will be returned (in IO
) as a tuple.
Exercises 


Getting Rid of the IO
MonadEdit
A disadvantage of randomIO
is that it requires us to utilize the IO
monad and store our state outside the program where we can't control what happens to it. We would prefer to only use IO when we really have a good reason to interact with the outside world.
To avoid the IO Monad, we can build a local generator. From the System.Random
module, we can use the random
and mkStdGen
functions to generate tuples containing a pseudorandom number together with a new generator to use the next time the function is called.
> :m System.Random > let generator = mkStdGen 0  "0" is our seed > generator 1 1 > random generator :: (Int, StdGen) (2092838931,1601120196 1655838864)
Now, we've avoided the IO Monad, but there are new problems. First and foremost, if we want to use generator
to get random numbers, the obvious definition...
> let randInt = fst . random $ generator :: Int > randInt 2092838931
...is useless; it will always give back the same value, 2092838931
, every time (because the same generator is always used). To solve this, we can take the second member of the tuple (i.e. the new generator) and feed it to a new call to random
:
> let (randInt, generator') = random generator :: (Int, StdGen) > randInt  Same value 2092838931 > random generator' :: (Int, StdGen)  Using new generator' returned from “random generator” (2143208520,439883729 1872071452)
Of course, this is clumsy and tedious. We need to keep creating new functions for new calls, and we're stuck with the fuss of having to carefully pass the generator around.
Dice without IOEdit
We can redo our dice throw with our new approach:
> randomR (1,6) (mkStdGen 0) (6, 40014 40692)
This tuple combines the result of throwing a single die with a new generator number. A simple implementation for throwing two dice is then:
clumsyRollDice :: (Int, Int) clumsyRollDice = (n, m) where (n, g) = randomR (1,6) (mkStdGen 0) (m, _) = randomR (1,6) g
Exercises 


The implementation of clumsyRollDice
works as a oneoff, but we have to manually write the passing of generator g
from one where
clause to the other. This approach will become increasingly cumbersome if we want to produce larger sets of pseudorandom numbers. It is also errorprone: what if we pass one of the middle generators to the wrong line in the where
clause?
What we really need is a way to automate the extraction of the second member of the tuple (i.e. the new generator) and feed it to a new call to random
. This is where the State
monad comes into the picture.
Introducing State
Edit
Note
In this chapter we will use the state monad provided by the module Control.Monad.Trans.State
of the transformers
package. By reading Haskell code in the wild, you will soon meet Control.Monad.State
, a module of the closely related mtl
package. The differences between these two modules need not concern us at the moment; everything we discuss here also applies to the mtl
variant.
The Haskell type State
describes functions that consume a state and produce a tuple that contains a result along with the new state after the result has been extracted.
The state function is wrapped by a data type definition which comes along with a runState
accessor so that pattern matching becomes unnecessary. For our current purposes, consider the definition equivalent to:^{[2]}
newtype State s a = State { runState :: s > (a, s) }
Here, s
is the type of the state, and a
the type of the produced result. Calling our type State
is arguably a bit of a misnomer because the wrapped value is not the state itself but a state processor.
newtypeEdit
Notice that we defined the data type with the newtype
keyword, rather than the usual data
. newtype
can be used only for types with just one constructor and just one field. It ensures that the trivial wrapping and unwrapping of the single field is eliminated by the compiler. For that reason, simple wrapper types such as State
are usually defined with newtype
. Would defining a synonym with type
be enough in such cases? Not really, because type
does not allow us to define instances for the new data type, which is what we are about to do...
Instantiating the MonadEdit
In contrast to the monads we have met thus far, State
has two type parameters. To define a Monad, we need to combine State
with a second parameter.
instance Monad (State s) where
So, there are many different State
monads including State String
, State Int
, State SomeLargeDataStructure
, and so on…
The return
function is implemented as:
return :: a > State s a return x = state ( \ st > (x, st) )
In words, giving a value to return
produces a function wrapped in the State
constructor. The function takes a state value, and returns it unchanged as the second member of a tuple, together with the specified result value.
Binding is a bit intricate:
(>>=) :: State s a > (a > State s b) > State s b processor >>= processorGenerator = state $ \ st > let (x, st') = runState processor st in runState (processorGenerator x) st'
(>>=)
is given a state processor and a function that can generate another processor using the result of the first one. The two processors are combined to obtain a function that takes the initial state, and returns the second result and state (i.e. after the second function has processed them).
The diagram shows this schematically, for a slightly different, but equivalent form of the ">>=" (bind) function, given below (where wpA and wpAB are wrapped versions of pA and pAB).
 pAB = s1 > pA > (v2,s2) > pB > (v3,s3) wpA >>= f = wpAB where wpAB = state $ \s1 > let pA = runState wpA (v2, s2) = pA s1 pB = runState $ f v2 (v3,s3) = pB s2 in (v3,s3)
Setting and Accessing the StateEdit
The monad instantiation allows us to manipulate various state processors, but you may at this point wonder where exactly the original state comes from in the first place. State s
is also an instance of the MonadState
class, which provides two additional functions:
put newState = state $ \_ > ((), newState)
Given a state, this function will generate a state processor. The processor's input will be disregarded, and the output will be a tuple carrying the state we provided. Since we do not care about the result (we are discarding the input, after all), the first element of the tuple will be "null".^{[3]}
The specular operation reads the state. This is accomplished by get
:
get = state $ \st > (st, st)
The resulting state processor produces the input st
in both positions of the output tuple (i.e. both as a result and as a state) so that it may be bound to other processors.
Getting Values and StateEdit
From the definition of State
, we know that runState
is an accessor to apply to a State a b
value to get the stateprocessing function. That function, given an initial state, will return the extracted value and the new state.
Other similar functions are evalState
and execState
. Given a State a b
and an initial state, the function evalState
will return the extracted value only, whereas execState
will return only the new state.
evalState :: State s a > s > a evalState processor st = fst ( runState processor st ) execState :: State s a > s > s execState processor st = snd ( runState processor st )
Dice and stateEdit
Let's use the State monad for our dice throw examples.
To avoid the confusion with "State" and "state processor", we'll use a type synonym:
import Control.Monad.Trans.State import System.Random type GeneratorState = State StdGen
So, GeneratorState Int
is in essence a StdGen > (Int, StdGen)
function and is a processor of the generator state. The generator state itself is produced by the mkStdGen
function. Note that GeneratorState
does not specify what type of values we are going to extract, only the type of the state.
We can now produce a function that, given a StdGen
generator, outputs a number between 1 and 6.
rollDie :: GeneratorState Int rollDie = do generator < get let (value, newGenerator) = randomR (1,6) generator put newGenerator return value
Let's go through each of the steps:
 First, we take out the pseudorandom generator with
<
in conjunction withget
.get
overwrites the monadic value (The 'a' in 'm a') with the state, binding the generator to the state. (If in doubt, recall the definition ofget
and>>=
above).  Then, we use the
randomR
function to produce an integer between 1 and 6 using the generator we took; we also store the new generator graciously returned byrandomR
.  We then set the state to be the
newGenerator
using theput
function, so that the next call will use a different pseudorandom generator;  Finally, we inject the result into the
GeneratorState
monad usingreturn
.
We can finally use our monadic die:
> evalState rollDie (mkStdGen 0) 6
Why have we involved monads and built such an intricate framework only to do exactly what fst $ randomR (1,6)
already does? Well, consider the following function:
rollDice :: GeneratorState (Int, Int) rollDice = liftM2 (,) rollDie rollDie
We obtain a function producing two pseudorandom numbers in a tuple. Note that these are in general different:
> evalState rollDice (mkStdGen 666) (6,1)
Under the hood, the monads are passing state to each other. It was previously very clunky using randomR (1,6)
because we had to pass state manually. Now, the monad is taking care of that for us. Assuming we know how to use the lifting functions, constructing intricate combinations of pseudorandom numbers (tuples, lists, whatever) has suddenly become much easier.
Exercises 


Pseudorandom values of different typesEdit
Until now, we considered only Int
as the type of the produced pseudorandom number. However, already when we defined the GeneratorState
monad, we saw that it did not specify anything about the type of the returned value. In fact, there is one implicit assumption: that we can produce values of such a type with a call to random
.
The Random
class (capitalized) includes default implementations for functions generating Int
, Char
, Integer
, Bool
, Double
and Float
, so you can immediately generate any of those.
Because GeneratorState
is "agnostic" in regard to the type of the pseudorandom value it produces, we can write a similarly "agnostic" function (analogous to rollDie
) that provides a pseudorandom value of unspecified type (as long as it is an instance of Random
):
getRandom :: Random a => GeneratorState a getRandom = do generator < get let (value, newGenerator) = random generator put newGenerator return value
Compared to rollDie
, this function does not specify the Int
type in its signature and uses random
instead of randomR
; otherwise, it is just the same. getRandom
can be used for any instance of Random
:
> evalState getRandom (mkStdGen 0) :: Bool True > evalState getRandom (mkStdGen 0) :: Char '\64685' > evalState getRandom (mkStdGen 0) :: Double 0.9872770354820595 > evalState getRandom (mkStdGen 0) :: Integer 2092838931
Indeed, it becomes quite easy to conjure all these at once:
allTypes :: GeneratorState (Int, Float, Char, Integer, Double, Bool, Int) allTypes = liftM (,,,,,,) getRandom `ap` getRandom `ap` getRandom `ap` getRandom `ap` getRandom `ap` getRandom `ap` getRandom
Here we are forced to used the ap
function, defined in Control.Monad
, since there exists no liftM7
(the standard libraries only go to liftM5
). As you can see, ap
fits multiple computations into an application of the (lifted) nelementtuple constructor (in this case the 7item (,,,,,,)
). To understand ap
further, look at its signature:
ap :: (Monad m) => m (a > b) > m a > m b
Remember then that type a
in Haskell can be a function as well as a value, and compare to:
>:type liftM (,,,,,,) getRandom liftM (,,,,,) getRandom :: (Random a1) => State StdGen (b > c > d > e > f > (a1, b, c, d, e, f))
The monad m
is obviously State StdGen
(which we "nicknamed" GeneratorState
), while ap
's first argument is function b > c > d > e > f > (a1, b, c, d, e, f)
. Applying ap
over and over (in this case 6 times), we finally get to the point where b
is an actual value (in our case, a 7element tuple), not another function. To sum it up, ap
applies a functioninamonad to a monadic value (compare with liftM
, which applies a function not in a monad to a monadic value).
So much for understanding the implementation. Function allTypes
provides pseudorandom values for all default instances of Random
; an additional Int
is inserted at the end to prove that the generator is not the same, as the two Int
s will be different.
> evalState allTypes (mkStdGen 0) (2092838931,9.953678e4,'\825586',868192881,0.4188001483955421,False,316817438)
Exercises 


NotesEdit
 ↑ There are also other ways to seed a pseudorandom number generator without using a global state for the program. For example, a program could have an algorithm that starts with a seed from checking the current date and time (assuming the computer's clock is functioning, this will never be a repeated value).
 ↑ The subtle issue with our approach is that the
transformers
package implements theState
type in a somewhat different way. The differences do not affect how we use or understandState
; as a consequence of them, however,Control.Monad.Trans.State
does not export aState
constructor. Rather, there is astate
function,state :: (s > (a, s)) > State s a
which does the same job. As for why the implementation is not the obvious one we presented above, we will get back to that a few chapters down the road.
 ↑ The technical term for the type of
()
is unit.