My friend, and fellow Professional F# 2.0 author, Ted Neward recently challenged me to a bit of a Code Kata. Take a list of numbers and compress it in a particular simple way but without any mutable state. What makes this problem interesting is that a tech interviewer mentioned that that he hadn’t seen a functional solution to this problem. I also wanted to share this because I think it’s a great example of how to convert an imperative loop into a functional fold.
So, on to the problem.
Given a set of numbers like [4; 5; 5; 5; 3; 3], compress it to be an alternating list of counts and numbers.
For example, the solution for [4; 5; 5; 5; 3; 3] would be [1; 4; 3; 5; 2; 3], as the list consists of one four, then three fives and finally two threes. Ordering counts as the initial list must be reconstructable from the compressed list. The answer to [4; 5; 4] would be [1; 4; 1; 5; 1; 4]
The imperative solution is rather simple, we use three variables outside of a loop: a last value, a count and an accumulator list.
let clImp list =
let mutable value = 0
let mutable count = 0
let output = new System.Collections.Generic.List<_>()
for element in list do
if element <> value && count > 0 then
output.Add(count)
output.Add(value)
count <- 0
value <- element
count <- count + 1
if count > 0 then
output.Add(count)
output.Add(value)
output
Using the normal .NET mutable list type, this version works efficiently and produces the output we expect.
> let compressed = clImp numbers Seq.iter (fun e -> printf "%i " e) compressed;; 1 4 3 5 2 3
How might we convert this to a functional style? In this example, the general type of operation could be thought of as the gradual building of a data structure while walking over a list. F# just happens to have a list processing function designed just for this task. This function is named fold and it is one of the most useful constructs in any functional programmer’s tool chest.
let clFold input = let (count, value, output) = List.fold (fun (count, value, output) element -> if element <> value && count > 0 then 1, element, output @ [count; value] else count + 1, element, output) (0 , 0, []) input output @ [count; value]
Here, we are doing almost exactly the same thing as in the imperative version but with a fold instead of a loop. The secret is that instead of putting variables outside of our loop and changing them with mutation, we have added them as elements in our accumulator tuple. In this way, the values are updated when each element is visited with no mutation.
However, there is one serious problem with this example. Appending to the end of a linked list requires recreating every node in that list. This will make our algorithm grow exponentially slower approximately in proportion to the length of the input list. To correct this we have two choices: do a head append with a normal fold and reverse the list when we are done, or use foldBack. The foldBack version is a rather small step from here and looks much nicer, so let’s go in that direction.
let clFoldBack input = let (count, value, output) = List.foldBack (fun element (count, value, output) -> if element <> value && count > 0 then 1, element, [count; value] @ output else count + 1, element, output) input (0, 0, []) [count; value] @ output
There are only two real changes here. First, we are using foldBack instead of fold. This change causes some argument reordering. Second, we are appending to the head of the output list instead of the tail. It works well, is rather fast and is easy to understand if you are comfortable with folds.
However, there is a bit of a dirty secret here. Under the hood foldBack converts its input list into an array when the size is large. As arrays have linear element look up time, they can be walked through backwards very quickly. Does this make the solution not functional? You’d never know unless you looked at the underlying implementation. Anyway, however you want to label it, it sure works well.
If you liked this example and want to see more check out our book, Professional F# 2.0. It’s just about to be done. In fact, I better get back to editing.
Enjoy this post? Continue the conversation with me on twitter.
Tags: Code Kata, F#, fold, foldBack, fsharp, Professional F# 2.0
The simplest thing I came up with using clojure:
(flatten (map (fn [ns] [(count ns) (first ns)]) (partition-by identity [4 5 5 3 6 6 6 6 7 8 8])))
Christophe Grand pointed me to “mapcat” and so my example becomes…
(mapcat (fn [ns] [(count ns) (first ns)]) (partition-by identity [4 5 5 3 6 6 6 6 7 8 8]))
Since you first posted I’ve been thinking about how to write a shorter version of this. Code Golf is always fun. However, F#’s partition is different from clojure’s (commutative), and so I can’t take that path.
Ultimately, it seems to me like no combination of the supplied library functions can come close to just that simple fold. I’d love to be proven wrong though.
// Playing with the Folding Challenge
let test = [4; 5; 5; 5; 3; 3]
let ans = [1; 4; 3; 5; 2; 3]
printfn “Goal: %A” ans
let compress l =
let compressor acc aNum =
match acc with
| count, theNum, soFar when aNum = theNum -> (count + 1, theNum, soFar)
| count, theNum, soFar when count > 0 -> (1, aNum, theNum :: count :: soFar)
| _, _, soFar -> (1, aNum, soFar)
let count, theNum, soFar = List.fold compressor (0, 0, []) l
if count = 0 then
List.rev soFar
else
List.rev (theNum :: count :: soFar)
let a = compress test
printfn “Res: %A” a
printfn “%b” (a = ans)
let partition_by (f : ‘a -> ‘b) (l : ‘a list) : ‘a list list =
let rec divider (soFar : ‘a list list) (last : ‘b option) (part : ‘a list) rest =
match rest, last with
| [], None -> soFar
| [], Some(b) -> part :: soFar
| x::more, None -> divider soFar (Some(f x)) [x] more
| x::more, Some(b) ->
let b2 = f x
if b = b2 then
divider soFar last (x :: part) more
else
divider ((List.rev part) :: soFar) (Some(b2)) [x] more
List.rev (divider [] None [] l)
let identity a = a
printfn “%A” (partition_by identity test)
let compress2 l =
l |>
partition_by identity |>
List.map (fun l -> [l.Length; l.Head]) |>
List.concat
printfn “%A” (compress2 test)
I was playing with the idea of taking a stab at writing those Clojure’s partitionby in F#. You’re way ahead of me I see :). By the way, there is an identity function built into F#. It’s called “id”.
Next, I need to figure out a way to preserve formatting in WordPress comments.
Mission successful.
This looks problem 10 from 99 Problems in Prolog (https://prof.ti.bfh.ch/hew1/informatik3/prolog/p-99/).
Googling “99 problems in X” where X is a language tends to end up with a solution for most languages :)
And a Haskell version for good measure:
[code]
import Data.List
f xs = group xs >>= \x -> [length x, head x]
main = print $ f [4,5,5,5,3,3] == [1,4,3,5,2,3]
[/code]
Scala version:
val foldFunction = {(result:List[Int], value:Int) =>
if (!result.isEmpty && value == result.head) {
value :: result.tail.head + 1 :: result.tail.tail
} else {
value :: 1 :: result
}
}
println(List(4,5,5,5,3,3).foldLeft(List[Int]())(foldFunction).reverse)
Using Perl 6:
my @in = 4, 5, 5, 5, 3, 3;
my $count = 0;
my @out = gather for @in Z [Mu, @in[0..@in.end-1]] -> $a, $b {
if $b && $b != $a {
take $count => $b;
$count = 1;
}
else {
$count++;
}
};
@out.push($count => @in[@in.end]);
@out.perl.say;
I am quite certain there is a much more elegant way than this, but my Perl 6 knowledge is very little and this is the best I can come up with.
It’s been pointed to me that using stateful variable like $count isn’t functional. Here’s another approach (hopefully functional enough) in Perl 6:
(
reduce {
$^a.elems
?? $^b != $^a[*-1]
?? [ $^a[*], 1, $^b ]
!! [ $^a[0..$^a.elems-3], $^a[*-2]+1, $^b ]
!! [ 1, $^b ]
}, [], 4, 5, 5, 5, 3, 3
).perl.say;
Here’s another Haskell solution, this time without
group. It’s a bit naughty, because it does a lot of list con- and destruction.foldr (
\(a, b) abs -> a:b:abs
) [] . foldr (
\a ps ->
case ps of
(n, a'):rest | a == a' -> (n + 1, a):rest
otherwise -> (1, a):ps
) []
Here is a one-liner in Scala:
Seq(4,5,5,5,3,3) groupBy identity flatMap { case (n, l) => Seq(l.size, n) }
=> List(1, 4, 2, 3, 3, 5)
Hi Eric, your one-liner is unfortunately incorrect since ‘groupBy’ produces groups irrespective of element sequencing.
Here’s another Scala variation, similar to Tim Dalton’s above,
def compress[T](l: List[T]) = l.foldLeft(List[(T, Int)]()) {
case (Nil, x) => (x, 1) :: Nil
case ((y, c) :: r, x) if (x == y) => (x, c+1) :: r
case (l, x) => (x, 1) :: l
}.reverse
scala> compress(List(4,5,5,5,3,3))
res0: List[(Int, Int)] = List((4,1), (5,3), (3,2))
// scala
def fold(list:List[Int]) = list.foldRight(List[Int]()){ (e, a) => a match {
case num :: `e` :: rest => num + 1 :: e :: rest
case _ => 1 :: e :: a
}
}
def reconstruct(list:List[Int]):List[Int] = list match {
case Nil => Nil
case num :: e :: rest => Nil.padTo(num, e) ::: reconstruct(rest)
}
val challenge1 = List(4, 5, 5, 5, 3, 3)
val result1 = List(1, 4, 3, 5, 2, 3)
val challenge2 = List(4, 5 , 4)
val result2 = List(1, 4, 1, 5, 1, 4)
fold(challenge1) == result1
fold(challenge2) == result2
reconstruct(result1) == challenge1
reconstruct(result2) == challenge2
Here’s a solution that I think is more idiomatic of functional programming, with an added benefit of being slightly shorter and possibly easier to understand.
let rle list =
let push n list =
match list with
| c :: m :: t when m=n -> (c+1)::m::t
| t -> 1::n::t
List.foldBack push list []
Very nice! Even more valuable would be a step-by-step explanation of your logical reasoning when writing that.
@Blake, your solution is the best listed here, concise and clear, thanks!
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