Is this function tail-recursive ?
let rec rec_algo1 step J =
if step = dSs then J
else
let a = Array.init (Array2D.length1 M) (fun i -> minby1J i M J)
let argmin = a|> Array.minBy snd |> fst
rec_algo1 (step+1) (argmin::J)
In general, is there a way to formally check it ?
Thanks.
This function is tail-recursive; I can tell by eyeballing it.
In general it is not always easy to tell. Perhaps the most reliable/pragmatic thing is just to check it on a large input (and make sure you are compiling in 'Release' mode, as 'Debug' mode turns off tail calls for better debugging).
Yes, you can formally prove that a function is tail-recursive. Every expression reduction has a tail-position, and if all recursions are in tail-positions then the function is tail-recursive. It's possible for a function to be tail-recursive in one place, but not in another.
In the expression let pat = exprA in exprB only exprB is in tail-position. That is, while you can go evaluate exprA, you still have to come back to evaluate exprB with exprA in mind. For every expression in the language, there's a reduction rule that tells you where the tail position is. In ExprA; ExprB it's ExprB again. In if ExprA then ExprB else ExprC it's both ExprB and ExprC and so on.
The compiler of course knows this as it goes. However the many expressions available in F# and the many internal optimizations carried out by the compiler as it goes, e.g. during pattern match compiling, computation expressions like seq{} or async{} can make knowing which expressions are in tail-position non-obvious.
Practically speaking, with some practice it's easy for small functions to determine a tail call by just looking at your nested expressions and checking the slots which are NOT in tail positions for function calls. (Remember that a tail call may be to another function!)
You asked how we can formally check this so I'll have a stab. We first have to define what it means for a function to be tail-recursive. A recursive function definition of the form
let rec f x_1 ... x_n = e
is tail recursive if all calls of f inside e are tail calls - ie. occur in a tail context. A tail context C is defined inductively as a term with a hole []:
C ::= []
| e
| let p = e in C
| e; C
| match e with p_1 -> C | ... | p_n -> C
| if e then C else C
where e is an F# expression, x is a variable and p is a pattern. We ought to expand this to mutually recursive function definitions but I'll leave that as an exercise.
Lets now apply this to your example. The only call to rec_algo1 in the body of the function is in this context:
if step = dSs then J
else
let a = Array.init (Array2D.length1 M) (fun i -> minby1J i M J)
let argmin = a|> Array.minBy snd |> fst
[]
And since this is a tail context, the function is tail-recursive. This is how functional programmers eyeball it - scan the body of the definition for recursive calls and then verify that each occurs in a tail context. A more intuitive definition of a tail call is when nothing else is done with the result of the call apart from returning it.
Related
I am trying to convert the following normal-recursive code to tail-recursive in F#, but I am failing miserably.
let rec insert elem lst =
match lst with
| [] -> [elem]
| hd::tl -> if hd > elem then
elem::lst
else
hd::(insert elem tl)
let lst1 = []
let lst2 = [1;2;3;5]
printfn "\nInserting 4 in an empty list: %A" (insert 4 lst1)
printfn "\nInserting 4 in a sorted list: %A" (insert 4 lst2)
Can you guys help? Unfortunately I am a beginner in f#. Also, can anyone point me to a good tutorial to understand tail-recursion?
The point of tail recursion is the following: the last operation before returning from a function is a call to itself; this is called a tail call, and is where tail recursion gets its name from (the recursive call is in last, i.e. tail position).
Your function is not tail recursive because at least one of its branches has an operation after the recursive call (the list cons operator).
The usual way of converting a recursive function into a tail-recursive function is to add an argument to accumulate intermediate results (the accumulator). When it comes to lists, and when you realize that the only elementary list operation is prepending an element, this also means that after you are through with processing your list, it will be reversed, and thus the resulting accumulator will usually have to be reversed again.
With all these points in mind, and given that we do not want to change the function's public interface by adding a parameter that is superfluous from the caller's point of view, we move the real work to an internal subfunction. This particular function is slightly more complicated because after the element has been inserted, there is nothing else to do but concatenate the two partial lists again, one of which is now in reverse order while the other is not. We create a second internal function to handle that part, and so the whole function looks as follows:
let insert elm lst =
let rec iter acc = function
| [] -> List.rev (elm :: acc)
| (h :: t) as ls ->
if h > elm then finish (elm :: ls) acc
else iter (h :: acc) t
and finish acc = function
| [] -> acc
| h :: t -> finish (h :: acc) t
iter [] lst
For further studying, Scott Wlaschin's F# for Fun and Profit is a great resource, and tail recursion is handled in a larger chapter about recursive types and more: https://fsharpforfunandprofit.com/posts/recursive-types-and-folds
According to this previous answer
You could implement List.map like this:
let rec map project = function
| [] -> []
| head :: tail ->
project head :: map project tail ;;
but instead, it is implemented like this:
let rec map project = function
| [] -> []
| head :: tail ->
let result = project head in
result :: map project tail ;;
They say that it is done this way to make sure the projection function is called in the expected order in case it has side effects, e.g.
map print_int [1;2;3] ;;
should print 123, but the first implementation would print 321. However, when I test both of them myself in OCaml and F#, they produce exactly the same 123 result.
(Note that I am testing this in the OCaml and F# REPLs--Nick in the comments suggests this might be the cause of my inability to reproduce, but why?)
What am I misunderstanding? Can someone elaborate why they should produce different orders and how I can reproduce? This runs contrary to my previous understanding of OCaml code I've written in the past so this was surprising to me and I want to make sure not to repeat the mistake. When I read the two, I read it as exactly the same thing with an extraneous intermediary binding.
My only guess is that the order of expression evaluation using cons is right to left, but that seems very odd?
This is being done purely as research to better understand how OCaml executes code, I don't really need to create my own List.map for production code.
The point is that the order of function application in OCaml is unspecified, not that it will be in some specific undesired order.
When evaluating this expression:
project head :: map project tail
OCaml is allowed to evaluate project head first or it can evaluate map project tail first. Which one it chooses to do is unspecified. (In theory it would probably be admissible for the order to be different for different calls.) Since you want a specified order, you need to use the form with let.
The fact that the order is unspecified is documented in Section 6.7 of the OCaml manual. See the section Function application:
The order in which the expressions expr, argument1, …, argumentn are evaluated is not specified.
(The claim that the evaluation order is unspecified isn't something you can test. No number of cases of a particular order prove that that order is always going to be chosen.)
So when you have an implementation of map like this:
let rec map f = function
| [] -> []
| a::l -> f a :: map f l
none of the function applications (f a) within the map calls are guaranteed to be evaluated sequentially in the order you'd expect. So when you try this:
map print_int [1;2;3]
you get the output
321- : unit list = [(); (); ()]
since by the time those function applications weren't executed in a specific order.
Now when you implement the map like this:
let rec map f = function
| [] -> []
| a::l -> let r = f a in r :: map f l
you're forcing the function applications to be executed in the order you're expecting because you explicitly make a call to evaluate let r = f a.
So now when you try:
map print_int [1;2;3]
you will get
123- : unit list = [(); (); ()]
because you've explicitly made an effort to evaluate the function applications in order.
I was trying to write a generic mapFoldWhile function, which is just mapFold but requires the state to be an option and stops as soon as it encounters a None state.
I don't want to use mapFold because it will transform the entire list, but I want it to stop as soon as an invalid state (i.e. None) is found.
This was myfirst attempt:
let mapFoldWhile (f : 'State option -> 'T -> 'Result * 'State option) (state : 'State option) (list : 'T list) =
let rec mapRec f state list results =
match list with
| [] -> (List.rev results, state)
| item :: tail ->
let (result, newState) = f state item
match newState with
| Some x -> mapRec f newState tail (result :: results)
| None -> ([], None)
mapRec f state list []
The List.rev irked me, since the point of the exercise was to exit early and constructing a new list ought to be even slower.
So I looked up what F#'s very own map does, which was:
let map f list = Microsoft.FSharp.Primitives.Basics.List.map f list
The ominous Microsoft.FSharp.Primitives.Basics.List.map can be found here and looks like this:
let map f x =
match x with
| [] -> []
| [h] -> [f h]
| (h::t) ->
let cons = freshConsNoTail (f h)
mapToFreshConsTail cons f t
cons
The consNoTail stuff is also in this file:
// optimized mutation-based implementation. This code is only valid in fslib, where mutation of private
// tail cons cells is permitted in carefully written library code.
let inline setFreshConsTail cons t = cons.(::).1 <- t
let inline freshConsNoTail h = h :: (# "ldnull" : 'T list #)
So I guess it turns out that F#'s immutable lists are actually mutable because performance? I'm a bit worried about this, having used the prepend-then-reverse list approach as I thought it was the "way to go" in F#.
I'm not very experienced with F# or functional programming in general, so maybe (probably) the whole idea of creating a new mapFoldWhile function is the wrong thing to do, but then what am I to do instead?
I often find myself in situations where I need to "exit early" because a collection item is "invalid" and I know that I don't have to look at the rest. I'm using List.pick or Seq.takeWhile in some cases, but in other instances I need to do more (mapFold).
Is there an efficient solution to this kind of problem (mapFoldWhile in particular and "exit early" in general) with functional programming concepts, or do I have to switch to an imperative solution / use a Collections.Generics.List?
In most cases, using List.rev is a perfectly sufficient solution.
You are right that the F# core library uses mutation and other dirty hacks to squeeze some more performance out of the F# list operations, but I think the micro-optimizations done there are not particularly good example. F# list functions are used almost everywhere so it might be a good trade-off, but I would not follow it in most situations.
Running your function with the following:
let l = [ 1 .. 1000000 ]
#time
mapFoldWhile (fun s v -> 0, s) (Some 1) l
I get ~240ms on the second line when I run the function without changes. When I just drop List.rev (so that it returns the data in the other order), I get around ~190ms. If you are really calling the function frequently enough that this matters, then you'd have to use mutation (actually, your own mutable list type), but I think that is rarely worth it.
For general "exit early" problems, you can often write the code as a composition of Seq.scan and Seq.takeWhile. For example, say you want to sum numbers from a sequence until you reach 1000. You can write:
input
|> Seq.scan (fun sum v -> v + sum) 0
|> Seq.takeWhile (fun sum -> sum < 1000)
Using Seq.scan generates a sequence of sums that is over the whole input, but since this is lazily generated, using Seq.takeWhile stops the computation as soon as the exit condition happens.
Once compiled and ran will this behave as a tail call?
let rec f accu = function
| [] -> accu
| h::t -> (h + accu) |> f <| t
Maybe there is an easy way to test behavior that I'm not aware of, but that might be another question.
I think this is much easier to see if you do not use the pipelining operator. In fact, the two pipelining operators are defined as inline, so the compiler will simplify the code to the following (and I think this version is also more readable and simpler to understand, so I would write this):
let rec f accu = function
| [] -> accu
| h::t -> f (h + accu) t
Now, you can read the definition of tail-call on Wikipedia, which says:
A tail call is a subroutine call that happens inside another procedure as its final action; it may produce a return value which is then immediately returned by the calling procedure.
So yes, the call to f on the last line is a tail-call.
If you wanted to analyze the original expression (h + accu) |> f <| t (without knowing that the pipelining operators are inlined), then this actually becomes ((h + accu) |> f) <| t. This means that the expression calls the <| operator with two arguments and returns the result - so the call to <| is a tail call.
The <| operator is defined like this:
let (<|) f a = f a
Now, the call to f inside the pipelining operator is also a tail-call (and similarly for the other pipelining operator).
In summary, if the compiler did not do inlining, you would have a sequence of three tail-calls (compiled using the .NET .tail instruction to make sure that .NET performs a tail-call). However, since the compiler performs inlining, it will see that you have a recursive tail-call (f calling f) and this can be more efficiently compiled into a loop. (But calls across multiple functions or operators cannot use loops that easily.)
If you look at the answer here, you will notice that f <| t is the same as f t (it only makes a difference if you put an expression in place of t which requires parenthesis).
Likewise x |> y is the same as y x.
This results in an equivalent expression which looks like this: f (h + accu) t, So (assuming the compiler doesn't have a bug or some such), your function should be tail recursive and most likely will be compiled down to a loop of some sort.
I was reading this post While or Tail Recursion in F#, what to use when? were several people say that the 'functional way' of doing things is by using maps/folds and higher order functions instead of recursing and looping.
I have this function that returns the item at position x in a list:
let rec getPos l c = if c = 0 then List.head l else getPos (List.tail l) (c - 1)
how can it be converted to be more functional?
This is a primitive list function (also known as List.nth).
It is okay to use recursion, especially when creating the basic building blocks. Although it would be nicer with pattern matching instead of if-else, like this:
let rec getPos l c =
match l with
| h::_ when c = 0 -> h
| _::t -> getPos t (c-1)
| [] -> failwith "list too short"
It is possible to express this function with List.fold, however the result is less clear than the recursive version.
I'm not sure what you mean by more functional.
Are you rolling this yourself as a learning exercise?
If not, you could just try this:
> let mylist = [1;2;3;4];;
> let n = 2;;
> mylist.[n];;
Your definition is already pretty functional since it uses a tail-recursive function instead of an imperative loop construct. However, it also looks like something a Scheme programmer might have written because you're using head and tail.
I suspect you're really asking how to write it in a more idiomatic ML style. The answer is to use pattern matching:
let rec getPos list n =
match list with
| hd::tl ->
if n = 0 then hd
else getPos tl (n - 1)
| [] -> failWith "Index out of range."
The recursion on the structure of the list is now revealed in the code. You also get a warning if the pattern matching is non-exhaustive so you're forced to deal with the index too big error.
You're right that functional programming also encourages the use of combinators like map or fold (so called points-free style). But too much of it just leads to unreadable code. I don't think it's warranted in this case.
Of course, Benjol is right, in practice you would just write mylist.[n].
If you'd like to use high-order functions for this, you could do:
let nth n = Seq.take (n+1) >> Seq.fold (fun _ x -> Some x) None
let nth n = Seq.take (n+1) >> Seq.reduce (fun _ x -> x)
But the idea is really to have basic constructions and combine them build whatever you want. Getting the nth element of a sequence is clearly a basic block that you should use. If you want the nth item, as Benjol mentioned, do myList.[n].
For building basic constructions, there's nothing wrong to use recursion or mutable loops (and often, you have to do it this way).
Not as a practical solution, but as an exercise, here is one of the ways to express nth via foldr or, in F# terms, List.foldBack:
let myNth n xs =
let step e f = function |0 -> e |n -> f (n-1)
let error _ = failwith "List is too short"
List.foldBack step xs error n