I'm taking a functional programming class and I'm having a hard time leaving the OOP mindset behind and finding answers to a lot of my questions.
I have to create a function that takes an ordered list and converts it into specified size sublists using a variation of fold.
This isn't right, but it's what I have:
splitList :: (Ord a) => Int -> [a] -> [[a]]
splitList size xs
| [condition] = foldr (\item subList -> item:subList) [] xs
| otherwise =
I've been searching and I found out that foldr is the variation that works better for what I want, and I think I've understood how fold works, I just don't know how I'll set up the guards so that when length sublist == size haskell resets the accumulator and goes on to the next list.
If I didn't explain myself correctly, here's the result I want:
> splitList 3 [1..10]
> [[1,2,3],[4,5,6],[7,8,9],[10]]
Thanks!
While Fabián's and chi's answers are entirely correct, there is actually an option to solve this puzzle using foldr. Consider the following code:
splitList :: Int -> [a] -> [[a]]
splitList n =
foldr (\el acc -> case acc of
[] -> [[el]]
(h : t) | length h < n -> (el : h) : t
_ -> [el] : acc
) []
The strategy here is to build up a list by extending its head as long as its length is lesser than desired. This solution has, however, two drawbacks:
It does something slightly different than in your example;
splitList 3 [1..10] produces [[1],[2,3,4],[5,6,7],[8,9,10]]
It's complexity is O(n * length l), as we measure length of up to n–sized list on each of the element which yields linear number of linear operations.
Let's first take care of first issue. In order to start counting at the beginning we need to traverse the list left–to–right, while foldr does it right–to–left. There is a common trick called "continuation passing" which will allow us to reverse the direction of the walk:
splitList :: Int -> [a] -> [[a]]
splitList n l = map reverse . reverse $
foldr (\el cont acc ->
case acc of
[] -> cont [[el]]
(h : t) | length h < n -> cont ((el : h) : t)
_ -> cont ([el] : acc)
) id l []
Here, instead of building the list in the accumulator we build up a function that will transform the list in the right direction. See this question for details. The side effect is reversing the list so we need to counter that by reverse application to the whole list and all of its elements. This goes linearly and tail-recursively tho.
Now let's work on the performance issue. The problem was that the length is linear on casual lists. There are two solutions for this:
Use another structure that caches length for a constant time access
Cache the value by ourselves
Because I guess it is a list exercise, let's go for the latter option:
splitList :: Int -> [a] -> [[a]]
splitList n l = map reverse . reverse . snd $
foldr (\el cont (countAcc, listAcc) ->
case listAcc of
[] -> cont (countAcc, [[el]])
(h : t) | countAcc < n -> cont (countAcc + 1, (el : h) : t)
(h : t) -> cont (1, [el] : (h : t))
) id l (1, [])
Here we extend our computational state with a counter that at each points stores the current length of the list. This gives us a constant check on each element and results in linear time complexity in the end.
A way to simplify this problem would be to split this into multiple functions. There are two things you need to do:
take n elements from the list, and
keep taking from the list as much as possible.
Lets try taking first:
taking :: Int -> [a] -> [a]
taking n [] = undefined
taking n (x:xs) = undefined
If there are no elemensts then we cannot take any more elements so we can only return an empty list, on the other hand if we do have an element then we can think of taking n (x:xs) as x : taking (n-1) xs, we would only need to check that n > 0.
taking n (x:xs)
| n > 0 = x :taking (n-1) xs
| otherwise = []
Now, we need to do that multiple times with the remainder so we should probably also return whatever remains from taking n elements from a list, in this case it would be whatever remains when n = 0 so we could try to adapt it to
| otherwise = ([], x:xs)
and then you would need to modify the type signature to return ([a], [a]) and the other 2 definitions to ensure you do return whatever remained after taking n.
With this approach your splitList would look like:
splitList n [] = []
splitList n l = chunk : splitList n remainder
where (chunk, remainder) = taking n l
Note however that folding would not be appropriate since it "flattens" whatever you are working on, for example given a [Int] you could fold to produce a sum which would be an Int. (foldr :: (a -> b -> b) -> b -> [a] -> b or "foldr function zero list produces an element of the function return type")
You want:
splitList 3 [1..10]
> [[1,2,3],[4,5,6],[7,8,9],[10]]
Since the "remainder" [10] in on the tail, I recommend you use foldl instead. E.g.
splitList :: (Ord a) => Int -> [a] -> [[a]]
splitList size xs
| size > 0 = foldl go [] xs
| otherwise = error "need a positive size"
where go acc x = ....
What should go do? Essentially, on your example, we must have:
splitList 3 [1..10]
= go (splitList 3 [1..9]) 10
= go [[1,2,3],[4,5,6],[7,8,9]] 10
= [[1,2,3],[4,5,6],[7,8,9],[10]]
splitList 3 [1..9]
= go (splitList 3 [1..8]) 9
= go [[1,2,3],[4,5,6],[7,8]] 9
= [[1,2,3],[4,5,6],[7,8,9]]
splitList 3 [1..8]
= go (splitList 3 [1..7]) 8
= go [[1,2,3],[4,5,6],[7]] 8
= [[1,2,3],[4,5,6],[7,8]]
and
splitList 3 [1]
= go [] 1
= [[1]]
Hence, go acc x should
check if acc is empty, if so, produce a singleton list [[x]].
otherwise, check the last list in acc:
if its length is less than size, append x
otherwise, append a new list [x] to acc
Try doing this by hand on your example to understand all the cases.
This will not be efficient, but it will work.
You don't really need the Ord a constraint.
Checking the accumulator's first sublist's length would lead to information flow from the right and the first chunk ending up the shorter one, potentially, instead of the last. Such function won't work on infinite lists either (not to mention the foldl-based variants).
A standard way to arrange for the information flow from the left with foldr is using an additional argument. The general scheme is
subLists n xs = foldr g z xs n
where
g x r i = cons x i (r (i-1))
....
The i argument to cons will guide its decision as to where to add the current element into. The i-1 decrements the counter on the way forward from the left, instead of on the way back from the right. z must have the same type as r and as the foldr itself as a whole, so,
z _ = [[]]
This means there must be a post-processing step, and some edge cases must be handled as well,
subLists n xs = post . foldr g z xs $ n
where
z _ = [[]]
g x r i | i == 1 = cons x i (r n)
g x r i = cons x i (r (i-1))
....
cons must be lazy enough not to force the results of the recursive call prematurely.
I leave it as an exercise finishing this up.
For a simpler version with a pre-processing step instead, see this recent answer of mine.
Just going to give another answer: this is quite similar to trying to write groupBy as a fold, and actually has a couple gotchas w.r.t. laziness that you have to bear in mind for an efficient and correct implementation. The following is the fastest version I found that maintains all the relevant laziness properties:
splitList :: Int -> [a] -> [[a]]
splitList m xs = snd (foldr f (const ([],[])) xs 1)
where
f x a i
| i <= 1 = let (ys,zs) = a m in ([], (x : ys) : zs)
| otherwise = let (ys,zs) = a (i-1) in (x : ys , zs)
The ys and the zs gotten from the recursive processing of the rest of list indicate the first and the rest of the groups into which the rest of the list will be broken up, by said recursive processing. So we either prepend the current element before that first subgroup if it is still shorter than needed, or we prepend before the first subgroup when it is just right and start a new, empty subgroup.
I'm still trying to understand how fold_left exactly works. Does it iterate through the list like List.iter? Or is there just something else wrong with my code? I'm thinking that e is the element in the list (so it's a tuple) and fst e takes the first element of the tuple and snd e takes the second element in the tuple.
let rec pow x n =
if n < 0 then
0
else if n = 0 then
1
else
x * pow x (n - 1);;
let polynomial lst = function
| x -> List.fold_left (fun e -> (fst e) * (pow x (snd e))) 1 lst;;
lst is a list of tuples where each tuple has two integers and makes a polynomial function, so polynomial is supposed to return a function. So an example of what should happen is this
# let f = polynomial [3, 3; -2, 1; 5, 0];;
val f : int -> int = <fun>
# f 2;; (* f is the polynomial function f(x) = 3x^3 + (-2)x + 5 *)
- : int = 25
But I get this error message
"Error: This expression has type int but an expression was expected of type 'a -> int * int".
List.fold_left indeed iterates over a list, passing the value from one call to another, which basically works like a bucket brigade, with only one bucket, where on each iteration you can look into the bucket, take whatever is there and put something new.
More formally, fold_left f init elements has type
val fold_left : ('a -> 'b -> 'a) -> 'a -> 'b list -> 'a
and it takes three arguments, the function f, the initial value init, and a list of elements. The function f is called for each element x of elements as f acc x, where acc is either init if x is the first element of the list or a result returned by the previous invocation of f. Back to our analogy, it is either the initial empty bucket or a bucket passed from the previous call in the chain.
In your case, the role of the bucket is the final sum of all terms. Initially, it is empty, then each new term computes (fst e) * (pow x (snd e)) and adds it to the bucket so that at the end you will have the sum of all terms,
let polynomial coeffs x =
List.fold_left (fun sum (k,r) -> sum + k * pow x r) 0 coeffs
Note, that instead of using fst and snd to access the elements of the pair, I deconstructed the tuple directly in the parameter list. This makes code easier to understand and shorter.
The function, that is applied on each step takes two arguments, sum is the bucket (it is often called the "accumulator") and the element of the list, which is a pair (k,r) in our case. We multiply k by the value of the x variable raised to the power r and then we add the result to the accumulator.
For people with an imperative mindset the following pseudocode1 might be more insightful than the bucket brigade analogy:
def fold_left(user_func, init, elements):
acc = init
for elt in elts:
acc = user_func(acc, elt)
return acc
1) Any resemblance to Python is purely coincidental :)
I am very new to F# and functional programming in general, and would like to recursively create a function that takes a list, and doubles all elements.
This is what I used to search for a spacific element, but im not sure how exactly I can change it to do what I need.
let rec returnN n theList =
match n, theList with
| 0, (head::_) -> head
| _, (_::theList') -> returnN (n - 1) theList'
| _, [] -> invalidArg "n" "n is larger then list length"
let list1 = [5; 10; 15; 20; 50; 25; 30]
printfn "%d" (returnN 3 list1 )
Is there a way for me to augment this to do what I need to?
I would like to take you through the thinking process.
Step 1. I need a recursive function that takes a list and doubles all the elements:
So, let's implement this in a naive way:
let rec doubleAll list =
match list with
| [] -> []
| hd :: tl -> hd * 2 :: doubleAll tl
Hopefully this logic is quite simple:
If we have an empty list, we return another empty list.
If we have a list with at least one element, we double the element and then prepend that to the result of calling the doubleAll function on the tail of the list.
Step 2. Actually, there are two things going on here:
I want a function that lets me apply another function to each element of a list.
In this case, I want that function to be "multiply by 2".
So, now we have two functions, let's do a simple implementation like this:
let rec map f list =
match list with
| [] -> []
| hd :: tl -> f hd :: map f tl
let doubleAll list = map (fun x -> x * 2) list
Step 3. Actually, the idea of map is such a common one that it's already built into the F# standard library, see List.map
So, all we need to do is this:
let doubleAll list = List.map (fun x -> x * 2) list
Hello i resolved problem with ealier task.
Now if i have for example list = [ 2; 3; 2 ; 6 ] want to translate it like this [2;5;7;13].
I declared x as my first element and xs as my rest and used List.scan . Idea below
(fun x n -> x + n) 0
but this make something like this
val it : int list = [0; 2; 5; 7; 13]
How to rewrite it to make list looking like this [2;5;7;13] with using any starting parameter. When i delete 0 i get error message.
Another question how it's going to look like List.Fold i tried to write something similar but it can get only sum of this list ;( .
Here's how I would do this with a fold (with type annotations):
let orig = [2; 3; 2; 6]
let workingSum (origList:int list) : int list =
let foldFunc (listSoFar: int list) (item:int) : int list =
let nextValue =
match listSoFar with
| [] -> item
| head::_ -> head + item
nextValue::listSoFar
origList |> List.fold foldFunc [] |> List.rev
For help learning fold, here's how I would do this with a recursive function:
let workingSum' (origList: int list): int list =
let rec loop (listSoFar: int list) (origListRemaining:int list): int list =
match origListRemaining with
| [] -> listSoFar
| remainHead::remainTail ->
let nextValue =
match listSoFar with
| [] -> remainHead
| head::_ -> head + remainHead
loop (nextValue::listSoFar) remainTail
origList |> loop [] |> List.rev
Note that the signature of the inner loop function is really similar to the foldFunc of the previous example, with one major difference: instead of being passed in the next element, it's being passed in the remainder of the original list that hasn't been processed yet. I'm using a match expression to account for the two different possibilities of that remainder of the original list: either the list is empty (meaning we're done), or it's not (and we need to return a recursive call to the next step).
I have to iterate over 2 lists. One starts off as a list of empty sublists and the second one has the max length for each of the sublists that are in the first one.
Example; list1 = [[];[];[];]; list2 = [1;2;3]
I need to fill out the empty sublists in list1 ensuring that the length of the sublists never exceed the corresponding integer in list2. To that end, I wrote the following function, that given an element, elem and 2 two lists list and list, will fill out the sublists.
let mapfn elem list1 list2=
let d = ref 1 in
List.map2 (fun a b -> if ((List.length a) < b) && (!d=1)
then (incr d ; List.append a [elem])
else a )
list1 list2
;;
I can now call this function repeatedly on the elements of a list and get the final answer I need
This function works as expected. But I am little bothered by the need to use the int ref d.
Is there a better way for me to do this.
I always find it worthwhile to split the problem into byte-sized pieces that can be composed together to form a solution. You want to pad or truncate lists to a given length; this is easy to do in two steps, first pad, then truncate:
let all x = let rec xs = x :: xs in xs
let rec take n = function
| [] -> []
| _ when n = 0 -> []
| x :: xs -> x :: take (pred n) xs
all creates an infinite list by repeating a value, while take extracts the prefix sublist of at most the given length. With these two, padding and truncating is very straightforwad:
let pad_trim e n l = take n (l # all e)
(it might be a bit surprising that this actually works in a strict language like OCaml). With that defined, your required function is simply:
let mapfn elem list1 list2 = List.map2 (pad_trim elem) list2 list1
that is, taking the second list as a list of specified lengths, pad each of the lists in the first list to that length with the supplied padding element. For instance, mapfn 42 [[];[];[]] [1;2;3] gives [[42]; [42; 42]; [42; 42; 42]]. If this is not what you need, you can tweak the parts and their assembly to suit your requirements.
Are you looking for something like that?
let fill_list elem lengths =
let rec fill acc = function
| 0 -> acc
| n -> fill (elem :: acc) (n - 1) in
let accumulators = List.map (fun _ -> []) lengths in
List.map2 fill accumulators lengths
(* toplevel test *)
# let test = fill_list 42 [1; 3];;
val test : int list list = [[42]; [42; 42; 42]]
(I couldn't make sense of the first list of empty lists in your question, but I suspect it may be the accumulators for the tail-rec fill function.)