It has classes
Consider this: if they have been designed correctly, they are working classes.
Procedural programming is counter-revolutionary because it has a global state.
Understanding abstract algebra is the reading theory of functional programming
Amateur! Category theory is the true way (or homotopy type theory if you wanna get wild).
TBH I consider category theory to be a branch of algebra. At least that's the class where it was first formally presented to me.
in theory, yes, but pedagogically it makes more sense as a branch of algebra
Man everytime I asked my Programming Languages professor what a monad was he would make punting motion.
It's really just that. A monad is really just a type that admits lawful definitions of
map
(so it's a functor),pure
andap
(so it's an applicative functor) andbind
(orflatmap
, though depending on your language this may be too powerful compared to a classicalbind
, cf. Scala). There's a lot of cool stuff that falls out of that, but if you understand "given a value in some context (of typem a
, say) and a function that lifts values into that context (possibly at a different type, i.e.,a -> m b
), I can produce a value in the same context (m b
, again, possibly at a different type)", there's not much else to look into. The functor, applicative, and monad laws dictate some of the operational characteristics of such definitions on a particular type, but that's really only relevant if you're making your own.Lifting is just a fancy word for (in this specific case, anyway) taking a concrete value and putting it some kind of computational context.
I have a value
1 :: Int
. There are a lot of contexts I can put that concrete value in! That is, I can make lots of values of typem Int
that represent different views on the same value.Let's start by looking at how functors (types that admit a lawful definition of
map
) lift values.map :: Functor f => (a -> b) -> (f a -> f b) -- equivalently (and more usually) (a -> b) -> f a -> f b
Normally, this is explained by saying "given a function
a -> b
and a value of typef a
for some functorf
, apply the function on eacha
inf a
and give me the result wrapped up inf
". Another way of explaining it (which I've written the type for preferentially) is "given a functiona -> b
, give me a functionf a -> f b
. We're lifting the entire function into whatever our functorial context is.Examples:
1 :: Int map (+1) :: Functor f => f Int -> f Int map show :: Functor f => f Int -> f String Just 1 :: Maybe Int map (+1) (Just 1) = Just 2 :: Maybe Int map show (Just 1) = Just "1" :: Maybe String [1] :: [Int] map (+1) [1] = [2] :: [Int] map show [1] = ["1"] :: [String]
Skipping directly up to monads, we really just need
pure :: Monad m => a -> m a
(a function which does nothing but put a value into a monadic [technically applicative] context) andbind :: Monad m => m a -> (a -> m b) -> m b
. To keep it simple, we'll just reproducemap
.-- We compose our function with pure to get their types into a shape that bind will understand incM :: Monad m => (Int -> Int) -> (Int -> m Int) incM = pure . (+1) showM :: Monad m => (Int -> String) -> (Int -> m String) showM = pure . show -- worth noting, Just 1 and [1] are both the same as pure 1 :: f Int with f fixed at Maybe and [], respectively bind (Just 1) incM = Just 2 bind (Just 1) showM = Just "1" bind [1] incM = [2] bind [1] showM = ["1"] bind (bind (Just 1) incM) showM = Just "2" -- bind (Just 2) showM bind (bind [1,2,3] incM) showM = ["2", "3", "4"] -- bind [2,3,4] showM
So you can see that this is a way of getting a value "out of" some context (like pulling the value of a Maybe or all the values out of a list), doing some sort of transformation on it, then wrapping it back up in the initial context; it also lets you chain these transformations, finally wrapping everything back up when you're done.
flatmap
is called that becausebind
for the list monad is exactly "map a function over this list and then flatten the list".bind ([[1,2,3],[4,5,6]]) id = [1,2,3,4,5,6] bind ([[1,2,3],[4,5,6]]) (map (+1)) = [2,3,4,5,6,7]
nah, they just pop up in a lot of contexts like futures/promises, errors, optionality, etc.. there's also a couple of neat tricks where you take a functor that represents a set of operations you'd like to provide and use an automatic construction to create a (free) monad out of it, thereby getting an interpreter for your DSL with no extra work.
I'll attempt a more thorough explanation, let me know if this makes any sense.
so I've got a type that represents some operations I want to provide:
data Op = Plus Int Int | Mul Int Int
I can turn that into a Functor by swapping the concrete values for a type variable:
data Op a = Plus a a | Mul a a
I'm doing this because I want to be able to compose these operations together - I should be able to freely sequence them however I like. so I can pass
Op
values in fora
and nest them as deep as I like. I can also write an interpreter forOp
values by breaking it down by cases and doing the obvious thing:eval :: OP Int ->Int eval (Plus a b) = a + b eval (Mul a b) = a * b
I give that type the obvious, dumb
Functor
instance, nothing special (exercise left for the reader). then, I can passOp
to a function (liftFree
) that turns it into a monad:liftFree :: Functor f => f a -> Free f a
(I'm going to skip the actual definition of
Free
as it's just a type out of the standard library)so I can use
liftFree
to turn the basic operations onOp
(Plus
andMul
) into monadic operations that are allowed to use do-notation:plus :: a -> a -> Free Op a plus a b = liftFree (Plus a b) mul :: a -> a -> Free Op a mul a b = lift Free (Mul a b) calculation :: Free Op Int calculation = do a <- plus 2 3 b <- mul a 5 plus a b
foldFree
then allows me to pass it an interpreter function that evaluates myOp
and turn it back into a regular value (like the obvious one I mentioned previously).foldFree :: Functor f => (f r -> r) -> Free f r -> r (foldFree eval calculation) :: Int
BUT because I can pass any interpreter I want, I've decoupled evaluation from the definition of the actions I'd like to take. so I could, instead of using an interpreter that calculated the final value, pass in one that pretty-printed it instead, or does a dry-run, etc..
prettyPrint :: Op String -> String foldFree prettyPrint (fmap show calculation)
so I can define actions that do a bunch of crazy IO stuff when called with a regular interpreter and run them instead with an interpreter that just sequences the operations and their arguments such that I can unit test that code without doing a bunch of mocking, etc.
I use a version of this trick wherever I can get away with it, even where I can't actually give a monad instance (like rust), because the decoupling alone is super powerful.
Along this line, I want to state that nobody has permission to downvote any of my posts.
Weakly typed languages are the way to go. Why should I have to declare a data type? Just let everything be a string and let the language figure out the context!
the only good hierarchy is a filesystem
Functional; haskell and AGDA are the way to go