I have the following function in OCaml:
let get_all_parents lst =
List.map (fun (name,opt) -> opt) lst
That maps my big list with (name, opt) to just a list of opt. An option can contain of either None or Some value which in this case is a string. I want a list of strings with all my values.
I am a beginner learning OCaml.
I don't think filter and map used together is a good solution to this problem. This is because when you apply map to convert your string option to string, you will have the None case to deal with. Even if you know that you won't have any Nones because you filtered them away, the type checker doesn't, and can't help you. If you have non-exhaustive pattern match warnings enabled, you will get them, or you will have to supply some kind of dummy string for the None case. And, you will have to hope you don't introduce errors when refactoring later, or else write test cases or do more code review.
Instead, you need a function filter_map : ('a -> 'b option) -> 'a list -> 'b list. The idea is that this works like map, except filter_map f lst drops each element of lst for which f evaluates to None. If f evaluates to Some v, the result list will have v. You could then use filter_map like so:
filter_map (fun (_, opt) -> opt) lst
You could also write that as
filter_map snd lst
A more general example would be:
filter_map (fun (_, opt) ->
match opt with
| Some s -> Some (s ^ "\n")
| None -> None)
lst
filter_map can be implemented like this:
let filter_map f lst =
let rec loop acc = function
| [] -> List.rev acc
| v::lst' ->
match f v with
| None -> loop acc lst'
| Some v' -> loop (v'::acc) lst'
in
loop [] lst
EDIT For greater completeness, you could also do
let filter_map f lst =
List.fold_left (fun acc v ->
match f v with
| Some v' -> v'::acc
| None -> acc) [] lst
|> List.rev
It's a shame that this kind of function isn't in the standard library. It's present in both Batteries Included and Jane Street Core.
I'm going to expand on #Carsten's answer. He is pointing you the right direction.
It's not clear what question you're asking. For example, I'm not sure why you're telling us about your function get_all_parents. Possibly this function was your attempt to get the answer you want, and that it's not quite working for you. Or maybe you're happy with this function, but you want to do some further processing on its results?
Either way, List.map can't do the whole job because it always returns a list of the same length as its input. But you need a list that can be different lengths, depending on how many None values there are in the big list.
So you need a function that can extract only the parts of a list that you're interested in. As #Carsten says, the key function for this is List.filter.
Some combination of map and filter will definitely do what you want. Or you can just use fold, which has the power of both map and filter. Or you can write your own recursive function that does all the work.
Update
Maybe your problem is in extracting the string from a string option. The "nice" way to do this is to provide a default value to use when the option is None:
let get default xo =
match xo with
| None -> default
| Some x -> x
# get "none" (Some "abc");;
- : string = "abc"
# get "none" None;;
- : string = "none"
#
type opt = Some of string | None
List.fold_left (fun lres -> function
(name,Some value) -> value::lres
| (name,None) -> lres
) [] [("s1",None);("s2",Some "s2bis")]
result:
- : string list = ["s2bis"]
Related
I have finally found an excellent entry point into functional programming with elm, and boy, do I like it, yet I still lack some probably fundamental elegance concerning a few concepts.
I often find myself writing code similar to the one below, which seems to be doing what it should, but if someone more experienced could suggest a more compact and direct approach, I am sure that could give some valuable insights into this so(u)rcery.
What I imagine this could boil down to, is something like the following
(<-> is a vector subtraction operator):
edgeDirections : List Vector -> List Vector
edgeDirections corners = List.map2 (\p v -> p <-> v) corners (shiftr 1 corners)
but I don't really have a satisfying approach to a method that would do a shiftr.
But the rules of stackoverflow demand it, here is what I tried. I wrote an ugly example of a possible usage for shiftr (I absolutely dislike the Debug.crash and I am not happy about the Maybe):
Given a list of vectors (the corner points of a polygon), calculate the directional vectors by calculating the difference of each corner-vector to its previous one, starting with the diff between the first and the last entry in the list.
[v1,v2,v3] -> [v1-v3,v2-v1,v3-v2]
Here goes:
edgeDir : Vector -> ( Maybe Vector, List Vector ) -> ( Maybe Vector, List Vector )
edgeDir p ( v, list ) =
case v of
Nothing ->
Debug.crash ("nono")
Just vector ->
( Just p, list ++ [ p <-> vector ] )
edgeDirections : List Vector -> List Vector
edgeDirections corners =
let
last =
List.head <| List.reverse corners
in
snd <| List.foldl edgeDir ( last, [] ) corners
main =
show <| edgeDirections [ Vector -1 0, Vector 0 1, Vector 1 0 ]
I appreciate any insight into how this result could be achieved in a more direct manner, maybe using existing language constructs I am not aware of yet, or any pointers on how to lessen the pain with Maybe. The latter may Just not be possible, but I am certain that the former will a) blow me away and b) make me scratch my head a couple times :)
Thank you, and many thanks for this felicitous language!
If Elm had built-in init and last functions, this could be cleaner.
You can get away from all those Maybes by doing some pattern matching. Here's my attempt using just pattern matching and an accumulator.
import List exposing (map2, append, reverse)
shiftr list =
let shiftr' acc rest =
case rest of
[] -> []
[x] -> x :: reverse acc
(x::xs) -> shiftr' (x::acc) xs
in shiftr' [] list
edgeDirections vectors =
map2 (<->) vectors <| shiftr vectors
Notice also the shortened writing of the mapping function of (<->), which is equivalent to (\p v -> p <-> v).
Suppose Elm did have an init and last function - let's just define those quickly here:
init list =
case list of
[] -> Nothing
[_] -> Just []
(x::xs) -> Maybe.map ((::) x) <| init xs
last list =
case list of
[] -> Nothing
[x] -> Just x
(_::xs) -> last xs
Then your shiftr function could be shortened to something like:
shiftr list =
case (init list, last list) of
(Just i, Just l) -> l :: i
_ -> list
Just after I "hung up", I came up with this, but I am sure this can still be greatly improved upon, if it's even correct (and it only works for n=1)
shiftr : List a -> List a
shiftr list =
let
rev =
List.reverse list
in
case List.head rev of
Nothing ->
list
Just t ->
[ t ] ++ (List.reverse <| List.drop 1 rev)
main =
show (shiftr [ 1, 2, 3, 4 ] |> shiftr)
I have the following excercise to do:
Code a function that will be a summation of a list of functions.
So I think that means that if a function get list of functions [f(x);g(x);h(x);...] it must return a function that is f(x)+g(x)+h(x)+...
I'm trying to do code that up for the general case and here's something I came up with:
let f_sum (h::t) = fold_left (fun a h -> (fun x -> (h x) + (a x))) h t;;
The problem is I'm using "+" operator and that means it works only when in list we have functions of type
'a -> int
So, can it be done more "generally", I mean can we write a function, that is a sum of ('a -> 'b) functions, given in a list?
yes, you can make plus function to be a parameter of your function, like
let f_sum plus fs =
let (+) = plus in
match fs with
| [] -> invalid_arg "f_sum: empty list"
| f :: fs -> fold_left ...
You can generalize even more, and ask a user to provide a zero value, so that you can return a function, returning zero if the list is empty. Also you can use records to group functions, or even first class modules (cf., Commutative_group.S in Core library).
I am trying to produce the solution for an intersection of two sets using tail recursion and an empty list [] as an accu:
let rec setintersect list list =
let rec setintersect2 a b c =
match a with
| [] -> (match b with [] -> (setsimplify c) | h::t -> (setsimplify c))
| h1::t1 -> (match b with [] -> (setsimplify c) |h2::t2 -> (if (elementof h1 b) then (setintersect2 t1 b (c#[h1])) else (setintersect2 t1 b c))) in
setintersect2 list list [];;
Elementof takes takes "an int and a list" and is correctly working to give true if x is an element of the list, false otherwise..
Here is the problem:
# setintersect [5;2;1] [2;6;9];;
- : int list = [2; 6; 9]
and it should give [2].
What am I doing wrong?
I feel like there's something really simple that I am misunderstanding!
Edit:
Thanks for the responses so far.
setsimplify just removes the duplicates.
so [2,2,3,5,6,6] becomes [2,3,5,6]. Tested and made sure it is working properly.
I am not supposed to use anything from the List library either. Also, I must use "tail recursion" with the accumulator being a list that I build as I go.
Here is the thought:
Check the head element in list1, IF it exists in list2, THEN recurse with the "tail of list1, list2, and list c with that element added to it". ELSE, then recurse with "tail of list1, list2 and list c(as it is)".
end conditions are either list1 or list2 are empty or both together are empty, return list c (as it is).
let rec setintersect list list = is wrong: the two arguments should be named differently (you should of course update the call to setintersect2 accordingly), otherwise the second will shadow the first. I would have thought that OCaml would have at least warned you about this fact, but it appears that it is not the case.
Apart from that, the code seems to do the trick. There are a couple of things that could be improved though:
setintersect itself is not recursive (only setintersect2 is), you thus don't need the rec
you should find a different name for the argument of setintersect2. In particular, it is not obvious which is the accumulator (acc or accu will be understood by most OCaml programmers in these circumstances).
c#[h1] is inefficient: you will traverse c completely each time you append an element. It's better to do h1::c and reverse the result at the end
As a bonus point, if you append element at the beginning of c, and assume that a is ordered, you don't have to call setsimplify at the end of the call: just check whether c is empty, and if this is not the case, append h1 only if it is not equal to the head of c.
First, You didn't list out your setsimplify function.
To write an ocaml function, try to split it first, and then combine if possible.
To solve this task, you just go through all elements in l1, and for every element, you check whether it is in l2 or not, right?
So definitely you need a function to check whether an element is in a list or not, right?
let make one:
let rec mem x = function
| [] -> false
| hd::tl -> hd = x || mem x tl
Then you can do your intersection:
let rec inter l1 l2 =
match l1 with
| [] -> []
| hd::tl -> if mem hd l2 then hd::(inter tl l2) else inter tl l2
Note that the above function is not tail-recursive, I guess you can change it to tail-recursive as an excise.
If you use std library, then it is simple:
let intersection l1 l2 = List.filter (fun x -> List.mem x l2) l1
I recently started with F# and implemented a very basic recursive function that represents the Sieve of Eratosthenes. I came up with the following, working code:
static member internal SieveOfEratosthenesRecursive sequence accumulator =
match sequence with
| [] -> accumulator
| head::tail -> let rest = tail |> List.filter(fun number -> number % head <> 0L)
let newAccumulator = head::accumulator
Prime.SieveOfEratosthenesRecursive rest newAccumulator
This function is not really memory efficient so I tried to eliminate the variables "rest" and "newAccumulator". I came up with the following code
static member internal SieveOfEratosthenesRecursive sequence accumulator =
match sequence with
| [] -> accumulator
| head::tail -> tail |> List.filter(fun number -> number % head <> 0L)
|> Prime.SieveOfEratosthenesRecursive (head::accumulator)
As far as I understand the tutorials I've read Prime.SieveOfEratosthenesRecursive will be called with the filtered tail as first parameter and a list consisting of head::accumulator as second one. However when I try to run the code with the reduced variable usage, the program gets trappen in an infinite loop. Why is this happening and what did I do wrong?
As far as I understand the tutorials I've read Prime.SieveOfEratosthenesRecursive will be called with the filtered tail as first parameter and a list consisting of head::accumulator as second one.
You have this backwards.
In the first version, you're passing rest then newAccumulator; in the second version, you're effectively passing newAccumulator then rest. I.e., you've transposed the arguments.
Prime.SieveOfEratosthenesRecursive (head::accumulator) is a partial function application wherein you're applying (head::accumulator) as the first argument (sequence). This partial function application yields a unary function (expecting accumulator), to which you are passing (via |>) what is called rest in the first version of your code.
Changing SieveOfEratosthenesRecursive's argument order is the easiest solution, but I would consider something like the following idiomatic as well:
static member internal SieveOfEratosthenesRecursive sequence accumulator =
match sequence with
| [] -> accumulator
| head::tail ->
tail
|> List.filter(fun number -> number % head <> 0L)
|> Prime.SieveOfEratosthenesRecursive <| (head::accumulator)
or
static member internal SieveOfEratosthenesRecursive sequence accumulator =
let inline flipzip a b = b, a
match sequence with
| [] -> accumulator
| head::tail ->
tail
|> List.filter(fun number -> number % head <> 0L)
|> flipzip (head::accumulator)
||> Prime.SieveOfEratosthenesRecursive
FWIW, eliminating rest and newAccumulator as named variables here is not going to impact your memory usage in the slightest.
The last call in your second function is equivalent to:
Prime.SieveOfEratosthenesRecursive newAccumulator rest
where you switch positions of two params. Since newAccumulator grows bigger after each recursive call, you will never reach the base case of empty list.
The rule of thumb is putting the most frequently changing parameter at last:
let rec sieve acc xs =
match xs with
| [] -> acc
| x::xs' -> xs' |> List.filter (fun y -> y % x <> 0L)
|> sieve (x::acc)
The above function could be shortened using function keyword:
let rec sieve acc = function
| [] -> acc
| x::xs' -> xs' |> List.filter (fun y -> y % x <> 0L)
|> sieve (x::acc)
Using pipe (|>) operator only makes the function more readable, it doesn't affect memory usage at all.
Take this example code (ignore it being horribly inefficient for the moment)
let listToString (lst:list<'a>) = ;;' prettify fix
let rec inner (lst:list<'a>) buffer = ;;' prettify fix
match List.length lst with
| 0 -> buffer
| _ -> inner (List.tl lst) (buffer + ((List.hd lst).ToString()))
inner lst ""
This is a common pattern I keep coming across in F#, I need to have an inner function who recurses itself over some value - and I only need this function once, is there in any way possible to call a lambda from within it self (some magic keyword or something) ? I would like the code to look something like this:
let listToString2 (lst:list<'a>) = ;;' prettify fix
( fun
(lst:list<'a>) buffer -> match List.length lst with ;;' prettify fix
| 0 -> buffer
| _ -> ##RECURSE## (List.tl lst) (buffer + ((List.hd lst).ToString()))
) lst ""
But as you might expect there is no way to refer to the anonymous function within itself, which is needed where I put ##RECURSE##
Yes, it's possible using so called y-combinators (or fixed-point combinators). Ex:
let rec fix f x = f (fix f) x
let fact f = function
| 0 -> 1
| x -> x * f (x-1)
let _ = (fix fact) 5 (* evaluates to "120" *)
I don't know articles for F# but this haskell entry might also be helpful.
But: I wouldn't use them if there is any alternative - They're quite hard to understand.
Your code (omit the type annotations here) is a standard construct and much more expressive.
let listToString lst =
let rec loop acc = function
| [] -> acc
| x::xs -> loop (acc ^ (string x)) xs
loop "" lst
Note that although you say you use the function only once, technically you refer to it by name twice, which is why it makes sense to give it a name.