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)
Given a set with n elements {1, 2, 3, ..., n}, I want to declare a function which returns the set containing the sets with k number of elements such as:
allSubsets 3 2
Would return [[1;2];[1;3];[2;3]] since those are the sets with 2 elements in a set created by 1 .. n
I've made the initial create-a-set-part but I'm a little stuck on how to find out all the subsets with k elements in it.
let allSubsets n k =
Set.ofList [1..n] |>
UPDATE:
I managed to get a working solution using yield:
let allSubsets n k =
let setN = Set.ofList [1..n]
let rec subsets s =
set [
if Set.count s = k then yield s
for e in s do
yield! subsets (Set.remove e s) ]
subsets setN
allSubsets 3 2
val it : Set<Set<int>> = set [set [1; 2]; set [1; 3]; set [2; 3]]
But isn't it possible to do it a little cleaner?
What you have is pretty clean, but it's also pretty inefficient. Try running allSubsets 10 3 and you'll know what I mean.
This is what I came up with:
let input = Set.ofList [ 1 .. 15 ]
let subsets (size:int) (input: Set<'a>) =
let rec inner elems =
match elems with
| [] -> [[]]
| h::t ->
List.fold (fun acc e ->
if List.length e < size then
(h::e)::e::acc
else e::acc) [] (inner t)
inner (Set.toList input)
|> Seq.choose (fun subset ->
if List.length subset = size then
Some <| Set.ofList subset
else None)
|> Set.ofSeq
subsets 3 input
The inner recursive function is a modified power set function from here. My first hunch was to generate the power set and then filter it, which would be pretty elegant, but that proved to be rather inefficient as well.
If this was to be production-quality code, I'd look into generating lists of indices of a given length, and use them to index into the input array. This is how FsCheck generates subsets, for example.
You can calculate the powerset and then filter in order to get only the ones with the specified length":
let powerset n k =
let lst = Set.toList n
seq [0..(lst.Length |> pown 2)-1]
|> Seq.map (fun i ->
set ([0..lst.Length-1] |> Seq.choose (fun x ->
if i &&& (pown 2 x) = 0 then None else Some lst.[x])))
|> Seq.filter (Seq.length >> (=) k)
However this is not efficient for large sets (n) of where k is close to n. But it's easy to optimize, you'll have to filter out early based on the digit count of the binary representation of each number.
This function implements the popular n-choose-k function:
let n_choose_k (arr: 'a []) (k: int) : 'a list list =
let len = Array.length arr
let rec choose lo x =
match x with
| 0 -> [[]]
| i -> [ for j in lo..(len-1) do
for ks in choose (j+1) (i-1) do
yield arr.[j]::ks ]
choose 0 k
> n_choose_k [|1..3|] 2;;
val it : int list list = [[1; 2]; [1; 3]; [2; 3]]
You can use Set.toArray and Set.ofList to convert to and from Set.
You can consider the following approach:
get powerset
let rec powerset xs =
match xs with
| [] -> [ [] ]
| h :: t -> List.fold (fun ys s -> (h :: s) :: s :: ys) [] (powerset t)
filter all subsets with a neccessary number of elements
let filtered xs k = List.filter (fun (x: 'a list) -> x.Length = k) xs
finally get the requested allSubsets
let allSubsets n k = Set.ofList (List.map (fun xs -> Set.ofList xs) (filtered (powerset [ 1 .. n ]) k))
Just to check and play with you can use:
printfn "%A" (allSubsets 3 2) // set [ set [1; 2]; set [1; 3]; set [2; 3] ]
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]
how do you convert an obj list to int type. I am trying to add two lists using a map function below but it doesn't work on obj lists.
let query f=
seq{
let cmd = new OleDbCommand( "SELECT * FROM F" );
let conn = new OleDbConnection( #"Provider=Microsoft.ACE.OLEDB.12.0;
Data Source=D:\Users\df\Documents\Vfolio.accdb;
Persist Security Info=False;" )
conn.Open()
let DAdapt = new OleDbDataAdapter("SELECT * FROM F",conn)
let DTab = new DataSet()
let i= DAdapt.Fill(DTab)
let rowCol = DTab.Tables.[0].Rows
let rowCount = rowCol.Count
for i in 0 .. (rowCount - 1) do
yield f (rowCol.[i])
}
let u= query(fun row -> row.[0])
let a= List.ofSeq u
let v=query(fun row -> row.[1])
let b= List.ofSeq v
let c = List.map2 (fun x y-> x + y) a b
error msg: The type 'obj' does not support the operator '+'
Because row.[i] returns type obj, your u and v become seq<obj>, and thus your a and b become type List<obj>, and therefore x and y are inferred to have type obj, and of course, you can't add two objs, which is exactly what the compiler tells you.
If you are sure that row.[0] and row.[1] are numbers of some kind, you should apply the appropriate cast, for example:
let u= query(fun row -> row.[0] :?> int)
let a= List.ofSeq u
let v=query(fun row -> row.[1] :?> int)
let b= List.ofSeq v
let c = List.map2 (fun x y-> x + y) a b
You can apply this cast in other places, too, depending on your taste and requirements, for example:
let c = List.map2 (fun x y-> (x :?> int) + (y :?> int)) a b
Or:
let a= u |> Seq.cast<int> |> List.ofSeq
let b= v |> Seq.cast<int> |> List.ofSeq
But I like the first example best, because it applies the cast at the earliest known point and results in the least amount of extra code.
But beware: if row.[0] turns out to be not an int at runtime, you will get an InvalidCastException.
P.S. In your List.map2 call, you could specify (+) directly instead of wrapping it in an extra lambda:
List.map2 (+) a b
P.P.S Also, it seems that your List.ofSeq calls are wasteful, for Seq also has a map2:
let u = query(fun row -> row.[0] :?> int)
let v = query(fun row -> row.[1] :?> int)
let c = Seq.map2 (+) u v |> List.ofSeq
P.P.P.S Also, have you noticed that each of the two calls to query generates its own DB connection, command, adapter, and dataset? Did you intend this or did you mean to only have one connection and then fetch different columns from the result? If so, you should only call query once:
let c = query( fun row -> (row.[0] :?> int) + (row.[1] :?> int) ) |> List.ofSeq
I want to create an F# function like List.find, but instead of searching for a single value, I want to search for any of the keys of a dictionary and return the corresponding dictionary value.
For example, this is a (poor) implementation of what I am trying to do.
let dict1=dict[(1,"A");(2,"B");(3,"C");(4,"D");(5,"E");(6,"F")]
let findInDict l =
let mutable found=false
let mutable value=""
for elem in l do
let f,v=dict1.TryGetValue(elem)
value<-if f && not found then v else value
found<-if not found then f else found
value
findInDict [9;2;5]
>
val dict1 : System.Collections.Generic.IDictionary<int,string>
val findInDict : l:seq<int> -> string
val it : string = "B"
What would be a functional equivalent?
A function for this almost feels like overkill. You can do this in one line using a list comprehension:
[for x in [9;4;5] do if dict1.ContainsKey x then yield dict1.[x]]
Edit:
After re-reading your question, I realized the above was not quite what you are looking for.
let rec findAValue l =
match l with
| [] -> None
| x::xs -> if dict1.ContainsKey x then Some(dict1.[x]) else findAValue xs
or more succinctly:
let rec findAValue = function
| [] -> None
| x::xs -> if dict1.ContainsKey x then Some(dict1.[x]) else findAValue xs
even more succinctly:
let findAValue = List.tryPick (fun x-> if dict1.ContainsKey x then Some(dict1.[x]) else None)
let highPerformanceFindAValue = List.tryPick (fun x-> match dict1.TryGetValue x with
| true, value->Some(value)
| _ -> None)
In the case where no value is found the result is None otherwise it's Some(value).
let findFirst l (dict: System.Collections.Generic.Dictionary<int, string>) =
let o = l |> List.tryFind (fun i -> dict.ContainsKey(i)) |> Option.map (fun k -> dict.[k])
match o with | None -> "" | Some(k) -> k
There are tons of ways to do this.
The obvious solution is to iterate, like you did:
let findInDict (d:IDictionary<'a, 'b>) l =
seq {
for key in l do
let f, v = d.TryGetValue(key)
if f then yield v
}
which is OK, I guess. It more or less mimics the typical step-wise approach.
You could rewrite this in terms of some sequence operators:
let findInDict1 (d:IDictionary<'a, 'b>) l =
Seq.filter (fun elem -> d.ContainsKey(elem)) l |> Seq.map (fun elem -> d.Item(elem))
which feels more functional, but is clearly doing way more work than it should be.
let findInDict2 (d:IDictionary<'a, 'b>) l =
Seq.choose(fun elem ->
let f,v = d.TryGetValue(elem)
if f then Some(v) else None) l
The last one makes the most sense in that we're only ever accessing the dictionary once per key and choose will do all the heavy lifting for us under the hood.