I heard about this construction which is loosely described as “a traversal represented in data, applied to some structure, without the need for the applicative”
It can be defined as:
data X a b r =
| Done r
| Step a (X a b (b -> r))
A word description would be as follows:
the type X a b r describes the shape of a structure
which contains things of type a
and for each a you get the opportunity to produce something of type b
and provided you do that for each a,
you get something of type r.
Thus a “traversal” of a list, [a], has type X a b [b], because if you can turn each a of the list into a b then you get a [b].
My question is: what is this thing called? Is there a reference to more information about it?
Example usage:
instance Functor (X a b) where
fmap f (Done r) = f r
fmap f (Step a next) = Step a (fmap (f .) next)
f :: [a] -> X a b [b]
f [] = Done []
f (a:as) = Step a (fmap (flip (:)) as)
g :: Applicative f => (a -> f b) -> X a b r -> f r
g f (Done r) = pure r
g f (Step a next) = g f next <*> f a
More generally:
instance Applicative (X a b) where
pure x = Done x
Done f <*> y = fmap (\y -> f y) y
Step a next <*> y = Step a (fmap flip next <*> y)
t :: Traversable t => t a -> X a b (t b)
t = traverse (\a -> Step a (Done id))
And, assuming I haven’t made any errors, we should find that:
flip g . t == traverse
Edit: I’ve thought about this some more. There is something this doesn’t have which a traversal has: a traversal can split up the computation into something that isn’t “one at a time,” for example to traverse a binary tree one can traverse the left and right half “in parallel.” Here is a structure that I think gives the same effect:
data Y a b r =
| Done r
| One a (b -> r)
| forall s t. Split (Y a b s) (Y a b t) (s -> t -> r)
(Slightly vague syntax as I don’t remember it and don’t want to write this as a gadt)
f1 :: X a b r -> Y a b r
f1 (Done x) = Done x
f1 (Step a next) = Split (One a id) (f1 next) (flip ($))
f2 :: Y a b r -> X a b r
f2 (Done x) = Done x
f2 (One a f) = Step a (Done f)
f2 (Split x y f) = f <$> f2 x <*> f2 y
Related
Say I have functions which accept the same parameters and I want to test if their outputs are equivalent for the same input.
f :: a -> b -> c
g :: a -> b -> c
f a b == g a b
How can I package the parameters a and b in x so I can write the following instead.
f x == g x
What are the best ways to accomplish this without needing to wrap the functions themselves?
The only way to do exactly what you’re asking is to use uncurry:
let
x = (a, b)
in uncurry f x == uncurry g x
(Or uncurryN for N arguments.)
However, instead of packaging the arguments in a tuple, you could use the (->) x instance of Applicative (i.e., functions taking x as input) to implicitly “spread” the arguments to the parameters of both functions, so at least you only have to mention them once. This instance is commonly used in point-free code.
For example, using liftA2 specialised to this instance:
-- General type:
liftA2 :: Applicative f => (a -> b -> c) -> f a -> f b -> f c
-- Specialised to ‘(->) x’ (using TypeApplications syntax):
liftA2 #((->) _) :: (a -> b -> c) -> (x -> a) -> (x -> b) -> (x -> c)
You get this pattern:
liftA2 h f g x
-- =
(h <$> f <*> g) x
-- =
h (f x) (g x)
To lift more arguments, you add another liftA2 or … <$> … <*> …:
liftA2 (liftA2 h) f g x y
-- =
(liftA2 h <$> f <*> g) x y
-- =
h (f x y) (g x y)
So in a case like yours:
f, g :: Int -> Char -> Bool
f i c = chr i == c
g i c = i == ord c
(liftA2 . liftA2) (==) f g :: Int -> Char -> Bool
-- =
liftA2 (liftA2 (==)) f g
-- =
(\ x y -> f x y == g x y)
The N in liftAN corresponds to the number of functions; the number of liftAN calls corresponds to the number of arguments.
Diving into Haskell, and while I am enjoying the language I'm finding the pointfree style completely illegible. I've come a across this function which only consists of these ASCII boobies as seen below.
f = (.)(.)
And while I understand its type signature and what it does, I can't for the life of me understand why it does it. So could someone please write out the de-pointfreed version of it for me, and maybe step by step work back to the pointfree version sorta like this:
f g x y = (g x) + y
f g x = (+) (g x)
f g = (+) . g
f = (.) (+)
Generally (?) (where ? stands for an arbitrary infix operator) is the same as \x y -> x ? y. So we can rewrite f as:
f = (\a b -> a . b) (\c d -> c . d)
Now if we apply the argument to the function, we get:
f = (\b -> (\c d -> c . d) . b)
Now b is just an argument to f, so we can rewrite this as:
f b = (\c d -> c . d) . b
The definition of . is f . g = \x -> f (g x). If replace the outer . with its definition, we get:
f b = \x -> (\c d -> c . d) (b x)
Again we can turn x into a regular parameter:
f b x = (\c d -> c . d) (b x)
Now let's replace the other .:
f b x = (\c d y -> c (d y)) (b x)
Now let's apply the argument:
f b x = \d y -> (b x) (d y)
Now let's move the parameters again:
f b x d y = (b x) (d y)
Done.
You can also gradually append arguments to f:
f = ((.) . )
f x = (.) . x
f x y = ((.) . x) y
= (.) (x y)
= ((x y) . )
f x y z = (x y) . z
f x y z t = ((x y) . z) t
= (x y) (z t)
= x y (z t)
= x y $ z t
The result reveals that x and z are actually (binary and unary, respectively) functions, so I'll use different identifiers:
f g x h y = g x (h y)
We can work backwards by "pattern matching" over the combinators' definitions. Given
f a b c d = a b (c d)
= (a b) (c d)
we proceed
= B (a b) c d
= B B a b c d -- writing B for (.)
so by eta-contraction
f = B B
because
a (b c) = B a b c -- bidirectional equation
by definition. Haskell's (.) is actually the B combinator (see BCKW combinators).
edit: Potentially, many combinators can match the same code. That's why there are many possible combinatory encodings for the same piece of code. For example, (ab)(cd) = (ab)(I(cd)) is a valid transformation, which might lead to some other combinator definition matching that. Choosing the "most appropriate" one is an art (or a search in a search space with somewhat high branching factor).
That's about going backwards, as you asked. But if you want to go "forward", personally, I like the combinatory approach much better over the lambda notation fidgeting. I would even just write many arguments right away, and get rid of the extra ones in the end:
BBabcdefg = B(ab)cdefg = (ab)(cd)efg
hence,
BBabcd = B(ab)cd = (ab)(cd)
is all there is to it.
I am trying to understand the Interchange law of applicative functor:
u <*> pure y = pure ($ y) <*> u
What make me confuse is, the function application $ y, consider following example:
($ 2) :: (a -> b) -> b
Why does the second argument get applied not the first?
That's an operator section. A few simple examples:
Prelude> (/2) <$> [1..8]
[0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0]
Prelude> (:"!") <$> ['a'..'e']
["a!","b!","c!","d!","e!"]
The section (:"!") is syntactic sugar for \c -> c:"!", i.e. it takes a character c and prepends it to the string "!".
Likewise, the section ($ 2) takes a function f and simply applies it to the number 2.
Note that this is different from ordinary partial application:
Prelude> ((/) 2) <$> [1..8]
[2.0,1.0,0.6666666666666666,0.5,0.4,0.3333333333333333,0.2857142857142857,0.25]
Here, I've simply applied the function (/) to one fixed argument 2, the dividend. This can also be written as a left section (2/). But the right section (/2) applies 2 as the divisor instead.
You can do that with operator sections. For example:
(5+ ) -- Same as \ x -> 5+x
( +5) -- Same as \ x -> x+5
It's only operators you can do this with; normal named functions can only be curried from left to right.
Haskell cheat sheet operator sections entry could be:
(a `op` b) = (a `op`) b = (`op` b) a = (op) a b
When op is an actual operator (not an alpha-numerical name), backticks aren't needed.
The above can be seen as partially applying implicitly defined lambda expressions:
(a `op`) b = (a `op` b) = (\y -> a `op` y) b = (\x y -> x `op` y) a b = op a b
(`op` b) a = (a `op` b) = (\x -> x `op` b) a = (\y x -> x `op` y) b a = flip op b a
If a function f expects more than two arguments eventually, we can similarly create its curried version by partially applying an explicit lambda expression:
(\y z x -> f x y z) b c -- = (\x -> f x b c)
(\x z y -> f x y z) a c -- = (\y -> f a y c)
(\x y z -> f x y z) a b -- = (\z -> f a b z)
The last case is equivalent to just f a b, and the second to (flip . f) a c:
g b c a = f a b c = flip f b a c = flip (flip f b) c a = (flip . flip f) b c a
g a c b = f a b c = flip (f a) c b = (flip . f) a c b
g a b c = f a b c
While musing what more useful standard class to suggest to this one
class Coordinate c where
createCoordinate :: x -> y -> c x y
getFirst :: c x y -> x
getSecond :: c x y -> y
addCoordinates :: (Num x, Num y) => c x y -> c x y -> c x y
it occured me that instead of something VectorSpace-y or R2, a rather more general beast might lurk here: a Type -> Type -> Type whose two contained types can both be extracted. Hm, perhaps they can be extracted?
Turns out neither the comonad nor bifunctors package contains something called Bicomonad. Question is, would such a class even make sense, category-theoretically? Unlike Bimonad (which also isn't defined, and I couldn't really see how might look), a naïve definition seems plausible:
class Bifunctor c => Bicomonad c where
fst :: c x y -> x
snd :: c x y -> y
bidup :: c x y -> c (c x y) (c x y)
probably with the laws
fst . bidup ≡ id
snd . bidup ≡ id
bimap fst snd . bidup ≡ id
bimap bidup bidup . bidup ≡ bidup . bidup
but I find it disquieting that both fields of the result of bidup contain the same type, and there are quite a number of other, perhaps “better” conceivable signatures.
Any thoughts?
This is not an answer, but for Bimonad, how about this?
class Biapplicative p => Bimonad p where
(>>==) :: p a b -> (a -> b -> p c d) -> p c d
biap :: Bimonad p => p (a -> b) (c -> d) -> p a c -> p b d
biap p q = p >>== \ab cd -> q >>== \a c -> bipure (ab a) (cd c)
instance Bimonad (,) where
(a,b) >>== f = f a b
I don't know if this is categorically right/interesting, or even remotely useful, but it smells right from a Haskell perspective. Does it match your Bicomonad or something similar?
I ended up figuring it out. See the video and slides of a talk I gave:
slides/pdf
video
Original question:
In my effort to understand generic recursion schemes (i.e., that use Fix) I have found it useful to write list-only versions of the various schemes. It makes it much easier to understand the actual schemes (without the additional overhead of the Fix stuff).
However, I have not yet figured out how to define list-only versions of zygo and futu.
Here are my specialised definitions so far:
cataL :: (a -> b -> b) -> b -> [a] -> b
cataL f b (a : as) = f a (cataL f b as)
cataL _ b [] = b
paraL :: (a -> [a] -> b -> b) -> b -> [a] -> b
paraL f b (a : as) = f a as (paraL f b as)
paraL _ b [] = b
-- TODO: histo
-- DONE: zygo (see below)
anaL :: (b -> (a, b)) -> b -> [a]
anaL f b = let (a, b') = f b in a : anaL f b'
anaL' :: (b -> Maybe (a, b)) -> b -> [a]
anaL' f b = case f b of
Just (a, b') -> a : anaL' f b'
Nothing -> []
apoL :: ([b] -> Maybe (a, Either [b] [a])) -> [b] -> [a]
apoL f b = case f b of
Nothing -> []
Just (x, Left c) -> x : apoL f c
Just (x, Right e) -> x : e
-- DONE: futu (see below)
hyloL :: (a -> c -> c) -> c -> (b -> Maybe (a, b)) -> b -> c
hyloL f z g = cataL f z . anaL' g
hyloL' :: (a -> c -> c) -> c -> (c -> Maybe (a, c)) -> c
hyloL' f z g = case g z of
Nothing -> z
Just (x,z') -> f x (hyloL' f z' g)
How do you define histo, zygo and futu for lists?
Zygomorphism is the high-falutin' mathsy name we give to folds built from two semi-mutually recursive functions. I'll give an example.
Imagine a function pm :: [Int] -> Int (for plus-minus) which intersperses + and - alternately through a list of numbers, such that pm [v,w,x,y,z] = v - (w + (x - (y + z))). You can write it out using primitive recursion:
lengthEven :: [a] -> Bool
lengthEven = even . length
pm0 [] = 0
pm0 (x:xs) = if lengthEven xs
then x - pm0 xs
else x + pm0 xs
Clearly pm0 is not compositional - you need to inspect the length of the whole list at each position to determine whether you're adding or subtracting. Paramorphism models primitive recursion of this sort, when the folding function needs to traverse the whole subtree at each iteration of the fold. So we can at least rewrite the code to conform to an established pattern.
paraL :: (a -> [a] -> b -> b) -> b -> [a] -> b
paraL f z [] = z
paraL f z (x:xs) = f x xs (paraL f z xs)
pm1 = paraL (\x xs acc -> if lengthEven xs then x - acc else x + acc) 0
But this is inefficient. lengthEven traverses the whole list at each iteration of the paramorphism resulting in an O(n2) algorithm.
We can make progress by noting that both lengthEven and para can be expressed as a catamorphism with foldr...
cataL = foldr
lengthEven' = cataL (\_ p -> not p) True
paraL' f z = snd . cataL (\x (xs, acc) -> (x:xs, f x xs acc)) ([], z)
... which suggests that we may be able to fuse the two operations into a single pass over the list.
pm2 = snd . cataL (\x (isEven, total) -> (not isEven, if isEven
then x - total
else x + total)) (True, 0)
We had a fold which depended on the result of another fold, and we were able to fuse them into one traversal of the list. Zygomorphism captures exactly this pattern.
zygoL :: (a -> b -> b) -> -- a folding function
(a -> b -> c -> c) -> -- a folding function which depends on the result of the other fold
b -> c -> -- zeroes for the two folds
[a] -> c
zygoL f g z e = snd . cataL (\x (p, q) -> (f x p, g x p q)) (z, e)
On each iteration of the fold, f sees its answer from the last iteration as in a catamorphism, but g gets to see both functions' answers. g entangles itself with f.
We'll write pm as a zygomorphism by using the first folding function to count whether the list is even or odd in length and the second one to calculate the total.
pm3 = zygoL (\_ p -> not p) (\x isEven total -> if isEven
then x - total
else x + total) True 0
This is classic functional programming style. We have a higher order function doing the heavy lifting of consuming the list; all we had to do was plug in the logic to aggregate results. The construction evidently terminates (you need only prove termination for foldr), and it's more efficient than the original hand-written version to boot.
Aside: #AlexR points out in the comments that zygomorphism has a big sister called mutumorphism, which captures mutual recursion in all
its glory. mutu generalises zygo in that both the folding
functions are allowed to inspect the other's result from the previous
iteration.
mutuL :: (a -> b -> c -> b) ->
(a -> b -> c -> c) ->
b -> c ->
[a] -> c
mutuL f g z e = snd . cataL (\x (p, q) -> (f x p q, g x p q)) (z, e)
You recover zygo from mutu simply by ignoring the extra argument.
zygoL f = mutuL (\x p q -> f x p)
Of course, all of these folding patterns generalise from lists to the fixed point of an arbitrary functor:
newtype Fix f = Fix { unFix :: f (Fix f) }
cata :: Functor f => (f a -> a) -> Fix f -> a
cata f = f . fmap (cata f) . unFix
para :: Functor f => (f (Fix f, a) -> a) -> Fix f -> a
para f = snd . cata (\x -> (Fix $ fmap fst x, f x))
zygo :: Functor f => (f b -> b) -> (f (b, a) -> a) -> Fix f -> a
zygo f g = snd . cata (\x -> (f $ fmap fst x, g x))
mutu :: Functor f => (f (b, a) -> b) -> (f (b, a) -> a) -> Fix f -> a
mutu f g = snd . cata (\x -> (f x, g x))
Compare the definition of zygo with that of zygoL. Also note that zygo Fix = para, and that the latter three folds can be implemented in terms of cata. In foldology everything is related to everything else.
You can recover the list version from the generalised version.
data ListF a r = Nil_ | Cons_ a r deriving Functor
type List a = Fix (ListF a)
zygoL' :: (a -> b -> b) -> (a -> b -> c -> c) -> b -> c -> List a -> c
zygoL' f g z e = zygo k l
where k Nil_ = z
k (Cons_ x y) = f x y
l Nil_ = e
l (Cons_ x (y, z)) = g x y z
pm4 = zygoL' (\_ p -> not p) (\x isEven total -> if isEven
then x - total
else x + total) True 0
Histomorphism models dynamic programming, the technique of tabulating the results of previous subcomputations. (It's sometimes called course-of-value induction.) In a histomorphism, the folding function has access to a table of the results of earlier iterations of the fold. Compare this with the catamorphism, where the folding function can only see the result of the last iteration. The histomorphism has the benefit of hindsight - you can see all of history.
Here's the idea. As we consume the input list, the folding algebra will output a sequence of bs. histo will jot down each b as it emerges, attaching it to the table of results. The number of items in the history is equal to the number of list layers you've processed - by the time you've torn down the whole list, the history of your operation will have a length equal to that of the list.
This is what the history of iterating a list(ory) looks like:
data History a b = Ancient b | Age a b (History a b)
History is a list of pairs of things and results, with an extra result at the end corresponding to the []-thing. We'll pair up each layer of the input list with its corresponding result.
cataL = foldr
history :: (a -> History a b -> b) -> b -> [a] -> History a b
history f z = cataL (\x h -> Age x (f x h) h) (Ancient z)
Once you've folded up the whole list from right to left, your final result will be at the top of the stack.
headH :: History a b -> b
headH (Ancient x) = x
headH (Age _ x _) = x
histoL :: (a -> History a b -> b) -> b -> [a] -> b
histoL f z = headH . history f z
(It happens that History a is a comonad, but headH (née extract) is all we need to define histoL.)
History labels each layer of the input list with its corresponding result. The cofree comonad captures the pattern of labelling each layer of an arbitrary structure.
data Cofree f a = Cofree { headC :: a, tailC :: f (Cofree f a) }
(I came up with History by plugging ListF into Cofree and simplifying.)
Compare this with the free monad,
data Free f a = Free (f (Free f a))
| Return a
Free is a coproduct type; Cofree is a product type. Free layers up a lasagne of fs, with values a at the bottom of the lasagne. Cofree layers up the lasagne with values a at each layer. Free monads are generalised externally-labelled trees; cofree comonads are generalised internally-labelled trees.
With Cofree in hand, we can generalise from lists to the fixpoint of an arbitrary functor,
newtype Fix f = Fix { unFix :: f (Fix f) }
cata :: Functor f => (f b -> b) -> Fix f -> b
cata f = f . fmap (cata f) . unFix
histo :: Functor f => (f (Cofree f b) -> b) -> Fix f -> b
histo f = headC . cata (\x -> Cofree (f x) x)
and once more recover the list version.
data ListF a r = Nil_ | Cons_ a r deriving Functor
type List a = Fix (ListF a)
type History' a b = Cofree (ListF a) b
histoL' :: (a -> History' a b -> b) -> b -> List a -> b
histoL' f z = histo g
where g Nil_ = z
g (Cons_ x h) = f x h
Aside: histo is the dual of futu. Look at their types.
histo :: Functor f => (f (Cofree f a) -> a) -> (Fix f -> a)
futu :: Functor f => (a -> f (Free f a)) -> (a -> Fix f)
futu is histo with the arrows flipped and with Free replaced by
Cofree. Histomorphisms see the past; futumorphisms predict the future.
And much like cata f . ana g can be fused into a hylomorphism,
histo f . futu g can be fused into a
chronomorphism.
Even if you skip the mathsy parts, this paper by Hinze and Wu features a good, example-driven tutorial on histomorphisms and their usage.
Since no one else has answered for futu yet, I'll try to stumble my way through. I'm going to use ListF a b = Base [a] = ConsF a b | NilF
Taking the type in recursion-schemes: futu :: Unfoldable t => (a -> Base t (Free (Base t) a)) -> a -> t.
I'm going to ignore the Unfoldable constraint and substitute [b] in for t.
(a -> Base [b] (Free (Base [b]) a)) -> a -> [b]
(a -> ListF b (Free (ListF b) a)) -> a -> [b]
Free (ListF b) a) is a list, possibly with an a-typed hole at the end. This means that it's isomorphic to ([b], Maybe a). So now we have:
(a -> ListF b ([b], Maybe a)) -> a -> [b]
Eliminating the last ListF, noticing that ListF a b is isomorphic to Maybe (a, b):
(a -> Maybe (b, ([b], Maybe a))) -> a -> [b]
Now, I'm pretty sure that playing type-tetris leads to the only sensible implementation:
futuL f x = case f x of
Nothing -> []
Just (y, (ys, mz)) -> y : (ys ++ fz)
where fz = case mz of
Nothing -> []
Just z -> futuL f z
Summarizing the resulting function, futuL takes a seed value and a function which may produce at least one result, and possibly a new seed value if it produced a result.
At first I thought this was equivalent to
notFutuL :: (a -> ([b], Maybe a)) -> a -> [b]
notFutuL f x = case f x of
(ys, mx) -> ys ++ case mx of
Nothing -> []
Just x' -> notFutuL f x'
And in practice, perhaps it is, more or less, but the one significant difference is that the real futu guarantees productivity (i.e. if f always returns, you will never be stuck waiting forever for the next list element).