A quick question on how to effectively group/filter list/seq.
Filter for only records where the optional field is not None
Remove the "option" parameter to make future processes easier (as None has been filtered out)
Group (this is of no problem I believe)
Am I using the best approach?
Thanks!
type tmp = {
A : string
B : int option }
type tmp2 = {
A : string
B : int }
let inline getOrElse (dft: 'a) (x: 'a option) =
match x with
| Some v -> v
| _ -> dft
let getGrouped (l: tmp list) =
l |> List.filter (fun a -> a.B.IsSome)
|> List.map (fun a -> {A = a.A ; B = (getOrElse 0 (a.B)) })
|> List.groupBy (fun a -> a.A)
The most natural approach for map+filter when option is involved is to use choose, which combines those two operations and drops the option wrapper from the filtered output.
Your example would look something like this:
let getGrouped (l: tmp list) =
l
|> List.choose (fun a ->
a.B
|> Option.map (fun b -> {A = a.A; B = b})
|> List.groupBy (fun a -> a.A)
The simple solution is just use the property that an option can be transformed to list with one or zero elements then you can define a function like:
let t1 ({A=a; B=b} : tmp) =
match b with
| (Some i) -> [{ A = a; B= i}]
| _ -> []
let getGrouped (l: tmp list) =
l |> List.collect t1
|> List.groupBy (fun a -> a.A)
I am interested to implement fold3, fold4 etc., similar to List.fold and List.fold2. e.g.
// TESTCASE
let polynomial (x:double) a b c = a*x + b*x*x + c*x*x*x
let A = [2.0; 3.0; 4.0; 5.0]
let B = [1.5; 1.0; 0.5; 0.2]
let C = [0.8; 0.01; 0.001; 0.0001]
let result = fold3 polynomial 0.7 A B C
// 2.0 * (0.7 ) + 1.5 * (0.7 )^2 + 0.8 * (0.7 )^3 -> 2.4094
// 3.0 * (2.4094) + 1.0 * (2.4094)^2 + 0.01 * (2.4094)^3 -> 13.173
// 4.0 * (13.173) + 0.5 * (13.173)^2 + 0.001 * (13.173)^3 -> 141.75
// 5.0 * (141.75) + 0.2 * (141.75)^2 + 0.0001 * (141.75)^3 -> 5011.964
//
// Output: result = 5011.964
My first method is grouping the 3 lists A, B, C, into a list of tuples, and then apply list.fold
let fold3 f x A B C =
List.map3 (fun a b c -> (a,b,c)) A B C
|> List.fold (fun acc (a,b,c) -> f acc a b c) x
// e.g. creates [(2.0,1.5,0.8); (3.0,1.0,0.01); ......]
My second method is to declare a mutable data, and use List.map3
let mutable result = 0.7
List.map3 (fun a b c ->
result <- polynomial result a b c // Change mutable data
// Output intermediate data
result) A B C
// Output from List.map3: [2.4094; 13.17327905; 141.7467853; 5011.963942]
// result mutable: 5011.963942
I would like to know if there are other ways to solve this problem. Thank you.
For fold3, you could just do zip3 and then fold:
let polynomial (x:double) (a, b, c) = a*x + b*x*x + c*x*x*x
List.zip3 A B C |> List.fold polynomial 0.7
But if you want this for the general case, then you need what we call "applicative functors".
First, imagine you have a list of functions and a list of values. Let's assume for now they're of the same size:
let fs = [ (fun x -> x+1); (fun x -> x+2); (fun x -> x+3) ]
let xs = [3;5;7]
And what you'd like to do (only natural) is to apply each function to each value. This is easily done with List.map2:
let apply fs xs = List.map2 (fun f x -> f x) fs xs
apply fs xs // Result = [4;7;10]
This operation "apply" is why these are called "applicative functors". Not just any ol' functors, but applicative ones. (the reason for why they're "functors" is a tad more complicated)
So far so good. But wait! What if each function in my list of functions returned another function?
let f1s = [ (fun x -> fun y -> x+y); (fun x -> fun y -> x-y); (fun x -> fun y -> x*y) ]
Or, if I remember that fun x -> fun y -> ... can be written in the short form of fun x y -> ...
let f1s = [ (fun x y -> x+y); (fun x y -> x-y); (fun x y -> x*y) ]
What if I apply such list of functions to my values? Well, naturally, I'll get another list of functions:
let f2s = apply f1s xs
// f2s = [ (fun y -> 3+y); (fun y -> 5+y); (fun y -> 7+y) ]
Hey, here's an idea! Since f2s is also a list of functions, can I apply it again? Well of course I can!
let ys = [1;2;3]
apply f2s ys // Result: [4;7;10]
Wait, what? What just happened?
I first applied the first list of functions to xs, and got another list of functions as a result. And then I applied that result to ys, and got a list of numbers.
We could rewrite that without intermediate variable f2s:
let f1s = [ (fun x y -> x+y); (fun x y -> x-y); (fun x y -> x*y) ]
let xs = [3;5;7]
let ys = [1;2;3]
apply (apply f1s xs) ys // Result: [4;7;10]
For extra convenience, this operation apply is usually expressed as an operator:
let (<*>) = apply
f1s <*> xs <*> ys
See what I did there? With this operator, it now looks very similar to just calling the function with two arguments. Neat.
But wait. What about our original task? In the original requirements we don't have a list of functions, we only have one single function.
Well, that can be easily fixed with another operation, let's call it "apply first". This operation will take a single function (not a list) plus a list of values, and apply this function to each value in the list:
let applyFirst f xs = List.map f xs
Oh, wait. That's just map. Silly me :-)
For extra convenience, this operation is usually also given an operator name:
let (<|>) = List.map
And now, I can do things like this:
let f x y = x + y
let xs = [3;5;7]
let ys = [1;2;3]
f <|> xs <*> ys // Result: [4;7;10]
Or this:
let f x y z = (x + y)*z
let xs = [3;5;7]
let ys = [1;2;3]
let zs = [1;-1;100]
f <|> xs <*> ys <*> zs // Result: [4;-7;1000]
Neat! I made it so I can apply arbitrary functions to lists of arguments at once!
Now, finally, you can apply this to your original problem:
let polynomial a b c (x:double) = a*x + b*x*x + c*x*x*x
let A = [2.0; 3.0; 4.0; 5.0]
let B = [1.5; 1.0; 0.5; 0.2]
let C = [0.8; 0.01; 0.001; 0.0001]
let ps = polynomial <|> A <*> B <*> C
let result = ps |> List.fold (fun x f -> f x) 0.7
The list ps consists of polynomial instances that are partially applied to corresponding elements of A, B, and C, and still expecting the final argument x. And on the next line, I simply fold over this list of functions, applying each of them to the result of the previous.
You could check the implementation for ideas:
https://github.com/fsharp/fsharp/blob/master/src/fsharp/FSharp.Core/array.fs
let fold<'T,'State> (f : 'State -> 'T -> 'State) (acc: 'State) (array:'T[]) =
checkNonNull "array" array
let f = OptimizedClosures.FSharpFunc<_,_,_>.Adapt(f)
let mutable state = acc
for i = 0 to array.Length-1 do
state <- f.Invoke(state,array.[i])
state
here's a few implementations for you:
let fold2<'a,'b,'State> (f : 'State -> 'a -> 'b -> 'State) (acc: 'State) (a:'a array) (b:'b array) =
let mutable state = acc
Array.iter2 (fun x y->state<-f state x y) a b
state
let iter3 f (a: 'a[]) (b: 'b[]) (c: 'c[]) =
let f = OptimizedClosures.FSharpFunc<_,_,_,_>.Adapt(f)
if a.Length <> b.Length || a.Length <> c.Length then failwithf "length"
for i = 0 to a.Length-1 do
f.Invoke(a.[i], b.[i], c.[i])
let altIter3 f (a: 'a[]) (b: 'b[]) (c: 'c[]) =
if a.Length <> b.Length || a.Length <> c.Length then failwithf "length"
for i = 0 to a.Length-1 do
f (a.[i]) (b.[i]) (c.[i])
let fold3<'a,'b,'State> (f : 'State -> 'a -> 'b -> 'c -> 'State) (acc: 'State) (a:'a array) (b:'b array) (c:'c array) =
let mutable state = acc
iter3 (fun x y z->state<-f state x y z) a b c
state
NB. we don't have an iter3, so, implement that. OptimizedClosures.FSharpFunc only allow up to 5 (or is it 7?) params. There are a finite number of type slots available. It makes sense. You can go higher than this, of course, without using the OptimizedClosures stuff.
... anyway, generally, you don't want to be iterating too many lists / arrays / sequences at once. So I'd caution against going too high.
... the better way forward in such cases may be to construct a record or tuple from said lists / arrays, first. Then, you can just use map and iter, which are already baked in. This is what zip / zip3 are all about (see: "(array1.[i],array2.[i],array3.[i])")
let zip3 (array1: _[]) (array2: _[]) (array3: _[]) =
checkNonNull "array1" array1
checkNonNull "array2" array2
checkNonNull "array3" array3
let len1 = array1.Length
if len1 <> array2.Length || len1 <> array3.Length then invalidArg3ArraysDifferent "array1" "array2" "array3" len1 array2.Length array3.Length
let res = Microsoft.FSharp.Primitives.Basics.Array.zeroCreateUnchecked len1
for i = 0 to res.Length-1 do
res.[i] <- (array1.[i],array2.[i],array3.[i])
res
I'm working with arrays at the moment, so my solution pertained to those. Sorry about that. Here's a recursive version for lists.
let fold3 f acc a b c =
let mutable state = acc
let rec fold3 f a b c =
match a,b,c with
| [],[],[] -> ()
| [],_,_
| _,[],_
| _,_,[] -> failwith "length"
| ahead::atail, bhead::btail, chead::ctail ->
state <- f state ahead bhead chead
fold3 f atail btail ctail
fold3 f a b c
i.e. we define a recursive function within a function which acts upon/mutates/changes the outer scoped mutable acc variable (a closure in functional speak). Finally, this gets returned.
It's pretty cool how much type information gets inferred about these functions. In the array examples above, mostly I was explicit with 'a 'b 'c. This time, we let type inference kick in. It knows we're dealing with lists from the :: operator. That's kind of neat.
NB. the compiler will probably unwind this tail-recursive approach so that it is just a loop behind-the-scenes. Generally, get a correct answer before optimising. Just mentioning this, though, as food for later thought.
I think the existing answers provide great options if you want to generalize folding, which was your original question. However, if I simply wanted to call the polynomial function on inputs specified in A, B and C, then I would probably do not want to introduce fairly complex constructs like applicative functors with fancy operators to my code base.
The problem becomes a lot easier if you transpose the input data, so that rather than having a list [A; B; C] with lists for individual variables, you have a transposed list with inputs for calculating each polynomial. To do this, we'll need the transpose function:
let rec transpose = function
| (_::_)::_ as M -> List.map List.head M :: transpose (List.map List.tail M)
| _ -> []
Now you can create a list with inputs, transpose it and calculate all polynomials simply using List.map:
transpose [A; B; C]
|> List.map (function
| [a; b; c] -> polynomial 0.7 a b c
| _ -> failwith "wrong number of arguments")
There are many ways to solve this problem. Few are mentioned like first zip3 all three list, then run over it. Using Applicate Functors like Fyodor Soikin describes means you can turn any function with any amount of arguments into a function that expects list instead of single arguments. This is a good general solution that works with any numbers of lists.
While this is a general good idea, i'm sometimes shocked that so few use more low-level tools. In this case it is a good idea to use recursion and learn more about recursion.
Recursion here is the right-tool because we have immutable data-types. But you could consider how you would implement it with mutable lists and looping first, if that helps. The steps would be:
You loop over an index from 0 to the amount of elements in the lists.
You check if every list has an element for the index
If every list has an element then you pass this to your "folder" function
If at least one list don't have an element, then you abort the loop
The recursive version works exactly the same. Only that you don't use an index to access the elements. You would chop of the first element from every list and then recurse on the remaining list.
Otherwise List.isEmpty is the function to check if a List is empty. You can chop off the first element with List.head and you get the remaining list with the first element removed by List.tail. This way you can just write:
let rec fold3 f acc l1 l2 l3 =
let h = List.head
let t = List.tail
let empty = List.isEmpty
if (empty l1) || (empty l2) && (empty l3)
then acc
else fold3 f (f acc (h l1) (h l2) (h l3)) (t l1) (t l2) (t l3)
The if line checks if every list has at least one element. If that is true
it executes: f acc (h l1) (h l2) (h l3). So it executes f and passes it the first element of every list as an argument. The result is the new accumulator of
the next fold3 call.
Now that you worked on the first element of every list, you must chop off the first element of every list, and continue with the remaining lists. You achieve that with List.tail or in the above example (t l1) (t l2) (t l3). Those are the next remaining lists for the next fold3 call.
Creating a fold4, fold5, fold6 and so on isn't really hard, and I think it is self-explanatory. My general advice is to learn a little bit more about recursion and try to write recursive List functions without Pattern Matching. Pattern Matching is not always easier.
Some code examples:
fold3 (fun acc x y z -> x + y + z :: acc) [] [1;2;3] [10;20;30] [100;200;300] // [333;222;111]
fold3 (fun acc x y z -> x :: y :: z :: acc) [] [1;2;3] [10;20;30] [100;200;300] // [3; 30; 300; 2; 20; 200; 1; 10; 100]
I should split seq<a> into seq<seq<a>> by an attribute of the elements. If this attribute equals by a given value it must be 'splitted' at that point. How can I do that in FSharp?
It should be nice to pass a 'function' to it that returns a bool if must be splitted at that item or no.
Sample:
Input sequence: seq: {1,2,3,4,1,5,6,7,1,9}
It should be splitted at every items when it equals 1, so the result should be:
seq
{
seq{1,2,3,4}
seq{1,5,6,7}
seq{1,9}
}
All you're really doing is grouping--creating a new group each time a value is encountered.
let splitBy f input =
let i = ref 0
input
|> Seq.map (fun x ->
if f x then incr i
!i, x)
|> Seq.groupBy fst
|> Seq.map (fun (_, b) -> Seq.map snd b)
Example
let items = seq [1;2;3;4;1;5;6;7;1;9]
items |> splitBy ((=) 1)
Again, shorter, with Stephen's nice improvements:
let splitBy f input =
let i = ref 0
input
|> Seq.groupBy (fun x ->
if f x then incr i
!i)
|> Seq.map snd
Unfortunately, writing functions that work with sequences (the seq<'T> type) is a bit difficult. They do not nicely work with functional concepts like pattern matching on lists. Instead, you have to use the GetEnumerator method and the resulting IEnumerator<'T> type. This often makes the code quite imperative. In this case, I'd write the following:
let splitUsing special (input:seq<_>) = seq {
use en = input.GetEnumerator()
let finished = ref false
let start = ref true
let rec taking () = seq {
if not (en.MoveNext()) then finished := true
elif en.Current = special then start := true
else
yield en.Current
yield! taking() }
yield taking()
while not (!finished) do
yield Seq.concat [ Seq.singleton special; taking()] }
I wouldn't recommend using the functional style (e.g. using Seq.skip and Seq.head), because this is quite inefficient - it creates a chain of sequences that take value from other sequence and just return it (so there is usually O(N^2) complexity).
Alternatively, you could write this using a computation builder for working with IEnumerator<'T>, but that's not standard. You can find it here, if you want to play with it.
The following is an impure implementation but yields immutable sequences lazily:
let unflatten f s = seq {
let buffer = ResizeArray()
let flush() = seq {
if buffer.Count > 0 then
yield Seq.readonly (buffer.ToArray())
buffer.Clear() }
for item in s do
if f item then yield! flush()
buffer.Add(item)
yield! flush() }
f is the function used to test whether an element should be a split point:
[1;2;3;4;1;5;6;7;1;9] |> unflatten (fun item -> item = 1)
Probably no the most efficient solution, but this works:
let takeAndSkipWhile f s = Seq.takeWhile f s, Seq.skipWhile f s
let takeAndSkipUntil f = takeAndSkipWhile (f >> not)
let rec splitOn f s =
if Seq.isEmpty s then
Seq.empty
else
let pre, post =
if f (Seq.head s) then
takeAndSkipUntil f (Seq.skip 1 s)
|> fun (a, b) ->
Seq.append [Seq.head s] a, b
else
takeAndSkipUntil f s
if Seq.isEmpty pre then
Seq.singleton post
else
Seq.append [pre] (splitOn f post)
splitOn ((=) 1) [1;2;3;4;1;5;6;7;1;9] // int list is compatible with seq<int>
The type of splitOn is ('a -> bool) -> seq<'a> -> seq>. I haven't tested it on many inputs, but it seems to work.
In case you are looking for something which actually works like split as an string split (i.e the item is not included on which the predicate returns true) the below is what I came up with.. tried to be as functional as possible :)
let fromEnum (input : 'a IEnumerator) =
seq {
while input.MoveNext() do
yield input.Current
}
let getMore (input : 'a IEnumerator) =
if input.MoveNext() = false then None
else Some ((input |> fromEnum) |> Seq.append [input.Current])
let splitBy (f : 'a -> bool) (input : 'a seq) =
use s = input.GetEnumerator()
let rec loop (acc : 'a seq seq) =
match s |> getMore with
| None -> acc
| Some x ->[x |> Seq.takeWhile (f >> not) |> Seq.toList |> List.toSeq]
|> Seq.append acc
|> loop
loop Seq.empty |> Seq.filter (Seq.isEmpty >> not)
seq [1;2;3;4;1;5;6;7;1;9;5;5;1]
|> splitBy ( (=) 1) |> printfn "%A"
if I have array A, and I have another bool array isChosen with the same length of A how can I build a new array from A where isChosen is true? something like A.[isChosen]? I cannot use Array.filter directly since isChosen is not a function of A elements and there is no Array.filteri like Array.mapi.
zip should help:
let l = [|1;2;3|]
let f = [|true; false; true|]
let r = [| for (v, f) in Seq.zip l f do if f then yield v|]
// or
let r = (l, f) ||> Seq.zip |> Seq.filter snd |> Seq.map fst |> Seq.toArray
Try the zip operator
seq.zip A isChosen
|> Seq.filter snd
|> Seq.map fst
|> Array.ofSeq
This will create a sequence of tuples where one value is from A and the other is from isChosen. This will pair the values together and make it very easy to filter them out in a Seq.filter expression
It's not as elegant or 'functional' as the other answers, but every once in a while I like a gentle reminder that you can use loops and array indices in F#:
let A = [|1;2;3|]
let isChosen = [|true; false; true|]
let r = [| for i in 0..A.Length-1 do
if isChosen.[i] then
yield A.[i] |]
printfn "%A" r
:)
And here are two more ways, just to demonstrate (even) more F# library functions:
let A = [|1;2;3|]
let isChosen = [|true;false;true|]
let B = Seq.map2 (fun x b -> if b then Some x else None) A isChosen
|> Seq.choose id
|> Seq.toArray
let C = Array.foldBack2 (fun x b acc -> if b then x::acc else acc) A isChosen []
|> List.toArray
My personal favorite for understandability (and therefore maintainability): desco's answer
let r = [| for (v, f) in Seq.zip l f do if f then yield v|]
Given a predicate "p", that tells if a solution is good enough. A cost function "f" that tells how good a possible solution is and a function that searches for the "best" (i.e. lowest cost) solution in a sequence of possible solutions. How does an idiomatic way to cancel the evaluation - in case the predicate ensures that the current solution is "good enough" - look like.
i.e. something like that:
let search p f solutionSpace =
solutionSpace |> Seq.map (fun x -> f x, x)
|> Seq.ignoreAllFollowingElementsWhenPredicateIsTrue (fun (c, s) -> p c)
|> Seq.minBy (fun (c, _) -> c)
This is called Seq.takeWhile in F# (when the predicate returns false, stop the sequence).
Example of use:
let search p f solutionSpace =
solutionSpace |> Seq.map (fun x -> f x, x)
|> Seq.takeWhile (fun (c, s) -> not (p c))
|> Seq.minBy (fun (c, _) -> c)