I am playing with a toy problem (Convex hull identification) and needed lexicographic sorting twice already. One of the cases was given a list of type Point = { X: float; Y: float }, I would like to sort by X coordinate, and in case of equality, by Y coordinate.
I ended up writing the following:
let rec lexiCompare comparers a b =
match comparers with
[ ] -> 0
| head :: tail ->
if not (head a b = 0) then head a b else
lexiCompare tail a b
let xComparer p1 p2 =
if p1.X > p2.X then 1 else
if p1.X < p2.X then -1 else
0
let yComparer p1 p2 =
if p1.Y > p2.Y then 1 else
if p1.Y < p2.Y then -1 else
0
let coordCompare =
lexiCompare [ yComparer; xComparer ]
Which allows me to do
let lowest (points: Point list) =
List.sortWith coordCompare points
|> List.head
So far, so good. However, this feels a bit heavy-handed. I have to create specific comparers returning -1, 0 or 1, and so far I can't see a straightforward way to use this in cases like List.minBy. Ideally, I would like to do something along the lines of providing a list of functions that can be compared (like [(fun p -> p.X); (fun p -> p.Y)]) and do something like lexicographic min of a list of items supporting that list of functions.
Is there a way to achieve this in F#? Or am I thinking about this incorrectly?
Is there a way to achieve this in F#? Or am I thinking about this incorrectly?
F# does this for you automatically when you define a record type like yours:
> type Point = { X: float; Y: float };;
type Point =
{X: float;
Y: float;}
You can immediately start comparing values. For example, defining a 3-element list of points and sorting it into lexicographic order using the built-in List.sort:
> [ { X = 2.0; Y = 3.0 }
{ X = 2.0; Y = 2.0 }
{ X = 1.0; Y = 3.0 } ]
|> List.sort;;
val it : Point list = [{X = 1.0;
Y = 3.0;}; {X = 2.0;
Y = 2.0;}; {X = 2.0;
Y = 3.0;}]
Note that the results were sorted first by X and then by Y.
You can compare two values of any comparable type using the built-in compare function.
If you want to use a custom ordering then you have two options. If you want to do all of your operations using your custom total order then it belongs in the type definition as an implementation of IComparable and friends. If you want to use a custom ordering for a few operations then you can use higher-order functions like List.sortBy and List.sortWith. For example, List.sortBy (fun p -> p.Y, p.X) will sort by Y and then X because F# generates the lexicographic comparison over 2-tuples for you (!).
This is one of the big advantages of F#.
Well, to start with, you can rely on F#'s built-in compare function:
let xComparer p1 p2 = compare p1.X p2.X
let yComparer p1 p2 = compare p1.Y p2.Y
Alternatively, you can clearly abstract this a bit if desired:
let compareWith f a b = compare (f a) (f b)
let xComparer = compareWith (fun p -> p.X)
let yComparer = compareWith (fun p -> p.Y)
Or, as you note, you could build this approach directly into the list handling function:
let rec lexiCompareWith l a b =
match l with
| [] -> 0
| f::fs ->
match compare (f a) (f b) with
| 0 -> lexiCompareWith fs a b
| n -> n
One important limitation here is that since you're putting them into a list, the functions must all have identical return types. This isn't a problem in your Point example (since both functions have type Point -> float), but it would prevent you from sorting two Person objects by name and then age (since the first projection would have type Person -> string but the second would have type Person -> int).
I don't think I understand your question correctly, but doesn't the following code work fine?
let lowest (points : Point list) = List.sort points |> List.head
It seems that F# performs implicit comparison on record data types. And my little experiment indicates that the comparison happens to be lexicographic. But I could not find any evidence to support that result.
So I'm not yet sure F# compares records lexicographically. I can still write in the following manner using tuple instead:
let lowest (points : Point list) =
let tuple = List.map (fun pt -> (pt.X, pt.Y)) points |> List.sort |> List.head
{ X = fst tuple; Y = snd tuple }
I hope this post could help.
Related
In C, I would solve the problem with a loop. To represent the idea, something like:
void foo(int x){
while(x > 0){
printf("%d", x % 10);
x /= 10;
}
}
With F#, I am unable to make the function return the single values. I tried:
let reverse =
let aux =
fun x ->
x % 10
let rec aux2 =
fun x ->
if x = 0 then 0
else aux2(aux(x / 10))
aux2 n
but it returns always the base case 0.
I cannot get my mind beyond this approach, where the recursion results are maintained with an operation, and cannot be reported (according to may comprehension) individually:
let reverse2 =
let rec aux =
fun x ->
if x = 0 then 0
else (x % 10) + aux (x / 10) // The operation returning the result
aux n
This is a simple exercise I am doing in order to "functionalize" my mind. Hence, I am looking for an approach to this problem not involving library functions.
A for loop that changes the value of mutable variables can be rewritten as a recursive function. You can think of the mutable variables as implicit parameters to the function. So if we have a mutable variable x, we need to instead pass the new state of x explicitly as a function parameter. The closest equivalent to your C function as a recursive F# function is this:
let rec foo x =
if x > 0 then
printf "%d" (x % 10)
foo (x / 10)
This in itself isn't particularly functional because it returns unit and only has side effects. You can collect the result of each loop using another parameter. This is often called an accumulator:
let foo x =
let rec loop x acc =
if x > 0 then
loop (x / 10) (x % 10 :: acc)
else acc
loop x [] |> List.rev
foo 100 // [0; 0; 1]
I made an inner loop function that is actually the recursive one. The outer foo function starts off the inner loop with [] as the accumulator. Items are added to the start of the list during each iteration and the accumulator list is reversed at the end.
You can use another type as the accumulator, e.g. a string, and append to the string instead of adding items to the list.
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 am looking for an idiomatic approach to programming filters in F#. For clarity, I refer to a filter as a function that uses a series of measurements over time and produces evolving estimates. This implies that the function be able to maintain state. For example, in Python one could use coroutines to maintain state in a very clean way.
What I'm looking for is an idiomatic approach to programming filters in F#. Given that my mind is thoroughly polluted with OOP and procedural principles, naturally I came up with classes to express them. Is there a more idiomatic approach to filtering in F#, one that could perhaps open up other benefits of the functional paradigm?
open System
open MathNet.Numerics.LinearAlgebra
open MathNet.Numerics.Random
open MathNet.Numerics.Distributions
open MathNet.Numerics.Statistics
open FSharp.Charting
type ScalarKalman (A : float, H : float, Q : float, R : float) = class
let mutable A = A
let mutable H = H
let mutable Q = Q
let mutable R = R
let mutable p = 0.
let mutable x = 0.
let mutable k = 0.
let mutable result = 0.
member this.X
with get() = x
and set(value) = x <- value
member this.P
with get() = p
and set(value) = p <- value
member this.K
with get() = k
and set(value) = k <- value
member this.update(newVal : float) =
let xp = A * this.X
let Pp = A * this.P * A + Q
this.K <- Pp * H / (H * Pp * H + R)
this.X <- xp + this.K * (newVal - H * xp)
this.P <- Pp - this.K * H * Pp
end
let n = 100
let obsv = [|for i in 0 .. n do yield 0.|]
let smv = [|for i in 0 .. n do yield 0.|]
let kal = new ScalarKalman(1., 1., 0., 5.)
kal.P <- 4.
kal.X <- 6.
for i in 0 .. n do
obsv.[i] <- Normal.Sample(10., 5.)
kal.update(obsv.[i])
smv.[i] <- kal.X
Chart.Combine([obsv |> Chart.FastLine
smv |> Chart.FastLine]) |> Chart.Show
In your case, the terms "functional" and "F# idiomatic" would consist of two things: immutable data and separation of data from code.
Immutable data: you would have one data structure representing the filter parameters (i.e. A, H, Q, and R), and another structure representing the filter's current state (i.e. X, K, and P). Both immutable. Instead of mutating the state, you would produce a new one.
Separation of data from code: the filter itself would consist of a single function that takes parameters, current state, next observation value, and produces next state. This next state will then be fed back into the function along with the next observation value, thus producing next+1 state, and so on. The parameters always stay constant, so they can be passed in just once, using partial application (see below).
Once you have such function, you can "apply" it to the list of observations as a "rolling projection", - as described above, - taking each observation and feeding it into the function along with the last state, producing the next state. This "rolling projection" operation is a very common thing in functional programming, and is usually called scan. F# does provide implementations of scan for all standard collections - list, seq, etc.
As a result of scan, you will have a list of filter's successive states. Now all that's left to do is to fish the X value out of each state.
Here is the complete solution:
module ScalarKalman =
type Parameters = { A : float; H : float; Q : float; R : float }
type State = { K: float; X: float; P: float }
let initState (s: State) = s
let getX s = s.X
let update parms state newVal =
let xp = parms.A * state.X
let Pp = parms.A * state.P * parms.A + parms.Q
let newK = Pp * parms.H / (parms.H * Pp * parms.H + parms.R)
{ K = newK
X = xp + newK * (newVal - parms.H * xp)
P = Pp - newK * parms.H * Pp }
let n = 100
let obsv = [for i in 0 .. n -> Normal.Sample(10., 5.)]
let kal = ScalarKalman.update { A = 1.; H = 1.; Q = 0.; R = 5. }
let initialState = ScalarKalman.initState { X = 6.; P = 4.; K = 0. }
let smv =
obsv
|> List.scan kal initialState
|> List.map ScalarKalman.getX
A note on design
Note the initState function declared in the module. This function may seem silly on the surface, but it has important meaning: it lets me specify state fields by name without opening the module, thus avoiding namespace pollution. Plus, the consuming code now looks more readable: it says what it does, no comments required.
Another common approach to this is to declare a "base" state in the module, which consuming code could then amend via the with syntax:
module ScalarKalman =
...
let zeroState = { K = 0.; X = 0.; P = 0. }
...
let initialState = { ScalarKalman.zeroState with X = 6.; P = 4. }
A note on collections
F# lists are fine on small amounts of data and small processing pipelines, but become expensive as these two dimensions grow. If you're working with a lot of streaming data, and/or if you're applying multiple filters in succession, you might be better off using lazy sequences - seq. To do so, simply replace List.scan and List.map with Seq.scan and Seq.map respectively. If you do, you will get a lazy sequence as the ultimate result, which you will then need to somehow consume - either convert it to a list, print it out, send it to the next component, or whatever your larger context implies.
I've recently started learning F#. I'm attempting to loop through a list of functions, applying each function to a value. For example, I have:
let identity x = fun x -> x
let square x = fun x -> x * x
let cube x = fun x -> x * x * x
let functions = [identity; square; cube]
I would now like to do something like the following:
let resultList = List.map(fun elem -> elem 3) functions
where the result value would be the list [3;9;27]. However, this is not what happens. Instead, I get:
val resultList : (int -> int) list = [<fun:Invoke#3000>; <fun:Invoke#3000>; <fun:Invoke#3000>]
I guess I'm not entirely convinced that using map is the right way forward any longer, so my questions are:
Why do I not get a list of numbers?
How would return a list of numbers?
What does <fun:Invoke> mean?
Thanks very much for your help.
Daniel
Your functions aren't quite correctly defined, they're taking an extra (unused) argument and are therefore just partially applied and not evaluated as you're expecting. Besides that, your thinking is correct;
let identity2 = fun x -> x
let square2 = fun x -> x * x
let cube2 = fun x -> x * x * x
let functions = [identity2; square2; cube2]
let resultList = List.map(fun elem -> elem 3) functions;;
> val resultList : int list = [3; 9; 27]
Although I'm not an F# expert, the <fun:Invoke> would in this case seem to indicate that the value is a (partially applied) function.
Because I like to simplify where I can, you can reduce a bit on Joachim's answer by removing the fun from your functions:
let identity x = x
let square x = x * x
let cube x = x * x * x
let functions = [identity; square; cube]
printfn "%A" (List.map(fun elem -> elem 3) functions)
Gives the output [3; 9; 27]
For me this is more natural. I didn't understand why the functions themselves needed to wrap funcs, rather than simply be the function.
I am currently experimenting with F#. The articles found on the internet are helpful, but as a C# programmer, I sometimes run into situations where I thought my solution would help, but it did not or just partially helped.
So my lack of knowledge of F# (and most likely, how the compiler works) is probably the reason why I am totally flabbergasted sometimes.
For example, I wrote a C# program to determine perfect numbers. It uses the known form of Euclids proof, that a perfect number can be formed from a Mersenne Prime 2p−1(2p−1) (where 2p-1 is a prime, and p is denoted as the power of).
Since the help of F# states that '**' can be used to calculate a power, but uses floating points, I tried to create a simple function with a bitshift operator (<<<) (note that I've edit this code for pointing out the need):
let PowBitShift (y:int32) = 1 <<< y;;
However, when running a test, and looking for performance improvements, I also tried a form which I remember from using Miranda (a functional programming language also), which uses recursion and a pattern matcher to calculate the power. The main benefit is that I can use the variable y as a 64-bit Integer, which is not possible with the standard bitshift operator.
let rec Pow (x : int64) (y : int64) =
match y with
| 0L -> 1L
| y -> x * Pow x (y - 1L);;
It turns out that this function is actually faster, but I cannot (yet) understand the reason why. Perhaps it is a less intellectual question, but I am still curious.
The seconds question then would be, that when calculating perfect numbers, you run into the fact that the int64 cannot display the big numbers crossing after finding the 9th perfectnumber (which is formed from the power of 31). I am trying to find out if you can use the BigInteger object (or bigint type) then, but here my knowledge of F# is blocking me a bit. Is it possible to create a powerfunction which accepts both arguments to be bigints?
I currently have this:
let rec PowBigInt (x : bigint) (y : bigint) =
match y with
| bigint.Zero -> 1I
| y -> x * Pow x (y - 1I);;
But it throws an error that bigint.Zero is not defined. So I am doing something wrong there as well. 0I is not accepted as a replacement, since it gives this error:
Non-primitive numeric literal constants cannot be used in pattern matches because they
can be mapped to multiple different types through the use of a NumericLiteral module.
Consider using replacing with a variable, and use 'when <variable> = <constant>' at the
end of the match clause.
But a pattern matcher cannot use a 'when' statement. Is there another solution to do this?
Thanks in advance, and please forgive my long post. I am only trying to express my 'challenges' as clear as I can.
I failed to understand why you need y to be an int64 or a bigint. According to this link, the biggest known Mersenne number is the one with p = 43112609, where p is indeed inside the range of int.
Having y as an integer, you can use the standard operator pown : ^T -> int -> ^T instead because:
let Pow (x : int64) y = pown x y
let PowBigInt (x: bigint) y = pown x y
Regarding your question of pattern matching bigint, the error message indicates quite clearly that you can use pattern matching via when guards:
let rec PowBigInt x y =
match y with
| _ when y = 0I -> 1I
| _ -> x * PowBigInt x (y - 1I)
I think the easiest way to define PowBigInt is to use if instead of pattern matching:
let rec PowBigInt (x : bigint) (y : bigint) =
if y = 0I then 1I
else x * PowBigInt x (y - 1I)
The problem is that bigint.Zero is a static property that returns the value, but patterns can only contain (constant) literals or F# active patterns. They can't directly contain property (or other) calls. However, you can write additional constraints in where clause if you still prefer match:
let rec PowBigInt (x : bigint) (y : bigint) =
match y with
| y when y = bigint.Zero -> 1I
| y -> x * PowBigInt x (y - 1I)
As a side-note, you can probably make the function more efficent using tail-recursion (the idea is that if a function makes recursive call as the last thing, then it can be compiled more efficiently):
let PowBigInt (x : bigint) (y : bigint) =
// Recursive helper function that stores the result calculated so far
// in 'acc' and recursively loops until 'y = 0I'
let rec PowBigIntHelper (y : bigint) (acc : bigint) =
if y = 0I then acc
else PowBigIntHelper (y - 1I) (x * acc)
// Start with the given value of 'y' and '1I' as the result so far
PowBigIntHelper y 1I
Regarding the PowBitShift function - I'm not sure why it is slower, but it definitely doesn't do what you need. Using bit shifting to implement power only works when the base is 2.
You don't need to create the Pow function.
The (**) operator has an overload for bigint -> int -> bigint.
Only the second parameter should be an integer, but I don't think that's a problem for your case.
Just try
bigint 10 ** 32 ;;
val it : System.Numerics.BigInteger =
100000000000000000000000000000000 {IsEven = true;
IsOne = false;
IsPowerOfTwo = false;
IsZero = false;
Sign = 1;}
Another option is to inline your function so it works with all numeric types (that support the required operators: (*), (-), get_One, and get_Zero).
let rec inline PowBigInt (x:^a) (y:^a) : ^a =
let zero = LanguagePrimitives.GenericZero
let one = LanguagePrimitives.GenericOne
if y = zero then one
else x * PowBigInt x (y - one)
let x = PowBigInt 10 32 //int
let y = PowBigInt 10I 32I //bigint
let z = PowBigInt 10.0 32.0 //float
I'd probably recommend making it tail-recursive, as Tomas suggested.