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I need to implement a simple dynamic programming algorithm in 2D in F#. For simple 1D cases Seq.unfold seems to be the way to go, see e.g. https://stackoverflow.com/a/7986083/5363
Is there a nice (and efficient) way to achieve a similar result in 2D, e.g. rewrite the following pseudo-code in functional style:
let alpha =
let result = Array2D.zeroCreate N T
for i in 0 .. N-1 do
result.[0, i] <- (initialPi i) * (b i observations.[0])
for t in 1 .. T-1 do
for i in 0 .. N-1 do
let s = row t-1 result |> Seq.mapi (fun j alpha_t_j -> alpha_t_j * initialA.[i, j]) () |> Seq.sum
result.[t, i] <- s * (b i observations.[t])
result
assume that all the missing functions and arrays are defined above.
EDIT: Actually read code, this is at least functional, does have a slightly different return type, although you could avoid that with a conversion
let alpha =
let rec build prev idx max =
match idx with
|0 ->
let r = (Array.init N (fun i -> (initialPi y) * (b i observations.[0]))
r:: (build r 1 max)
|t when t=max -> []
|_ ->
let s = prev |> Seq.mapi (fun j alpha_t_j -> alpha_t_j * initialA.[i, j]) () |> Seq.sum
let r = Array.init N (fun i -> s * (b i observations.[t]))
r:: build r (idx+1 max)
build [] 0 T |> List.toArray
My code (below) falls over with a stack overflow exception. Im assuming F# isnt like haskell and dosent play well with recursive lists. Whats the correct way of dealing with recursive lists like this in F# ? Should i pass it an int so it has a determined size?
let rec collatz num =
match num with
|x when x % 2 = 0 ->num :: collatz (x/2)
|x -> num :: collatz ((x * 3) + 1)
let smallList = collatz(4) |> Seq.take(4)
For an infinite list like this, you want to return a sequence. Sequences are lazy; lists are not.
let rec collatz num =
seq {
yield num
match num with
| x when x % 2 = 0 -> yield! collatz (x/2)
| x -> yield! collatz ((x * 3) + 1)
}
let smallList =
collatz 4
|> Seq.take 4
|> Seq.toList //[4; 2; 1; 4]
let collatz num =
let next x = if x % 2 = 0 then x / 2 else x * 3 + 1
(num, next num)
|>Seq.unfold (fun (n, x) -> Some (n, (x, next x)))
Is it possible to combine memoization and tail-recursion somehow? I'm learning F# at the moment and understand both concepts but can't seem to combine them.
Suppose I have the following memoize function (from Real-World Functional Programming):
let memoize f = let cache = new Dictionary<_, _>()
(fun x -> match cache.TryGetValue(x) with
| true, y -> y
| _ -> let v = f(x)
cache.Add(x, v)
v)
and the following factorial function:
let rec factorial(x) = if (x = 0) then 1 else x * factorial(x - 1)
Memoizing factorial isn't too difficult and making it tail-recursive isn't either:
let rec memoizedFactorial =
memoize (fun x -> if (x = 0) then 1 else x * memoizedFactorial(x - 1))
let tailRecursiveFactorial(x) =
let rec factorialUtil(x, res) = if (x = 0)
then res
else let newRes = x * res
factorialUtil(x - 1, newRes)
factorialUtil(x, 1)
But can you combine memoization and tail-recursion? I made some attempts but can't seem to get it working. Or is this simply not possible?
As always, continuations yield an elegant tailcall solution:
open System.Collections.Generic
let cache = Dictionary<_,_>() // TODO move inside
let memoizedTRFactorial =
let rec fac n k = // must make tailcalls to k
match cache.TryGetValue(n) with
| true, r -> k r
| _ ->
if n=0 then
k 1
else
fac (n-1) (fun r1 ->
printfn "multiplying by %d" n //***
let r = r1 * n
cache.Add(n,r)
k r)
fun n -> fac n id
printfn "---"
let r = memoizedTRFactorial 4
printfn "%d" r
for KeyValue(k,v) in cache do
printfn "%d: %d" k v
printfn "---"
let r2 = memoizedTRFactorial 5
printfn "%d" r2
printfn "---"
// comment out *** line, then run this
//let r3 = memoizedTRFactorial 100000
//printfn "%d" r3
There are two kinds of tests. First, this demos that calling F(4) caches F(4), F(3), F(2), F(1) as you would like.
Then, comment out the *** printf and uncomment the final test (and compile in Release mode) to show that it does not StackOverflow (it uses tailcalls correctly).
Perhaps I'll generalize out 'memoize' and demonstrate it on 'fib' next...
EDIT
Ok, here's the next step, I think, decoupling memoization from factorial:
open System.Collections.Generic
let cache = Dictionary<_,_>() // TODO move inside
let memoize fGuts n =
let rec newFunc n k = // must make tailcalls to k
match cache.TryGetValue(n) with
| true, r -> k r
| _ ->
fGuts n (fun r ->
cache.Add(n,r)
k r) newFunc
newFunc n id
let TRFactorialGuts n k memoGuts =
if n=0 then
k 1
else
memoGuts (n-1) (fun r1 ->
printfn "multiplying by %d" n //***
let r = r1 * n
k r)
let memoizedTRFactorial = memoize TRFactorialGuts
printfn "---"
let r = memoizedTRFactorial 4
printfn "%d" r
for KeyValue(k,v) in cache do
printfn "%d: %d" k v
printfn "---"
let r2 = memoizedTRFactorial 5
printfn "%d" r2
printfn "---"
// comment out *** line, then run this
//let r3 = memoizedTRFactorial 100000
//printfn "%d" r3
EDIT
Ok, here's a fully generalized version that seems to work.
open System.Collections.Generic
let memoize fGuts =
let cache = Dictionary<_,_>()
let rec newFunc n k = // must make tailcalls to k
match cache.TryGetValue(n) with
| true, r -> k r
| _ ->
fGuts n (fun r ->
cache.Add(n,r)
k r) newFunc
cache, (fun n -> newFunc n id)
let TRFactorialGuts n k memoGuts =
if n=0 then
k 1
else
memoGuts (n-1) (fun r1 ->
printfn "multiplying by %d" n //***
let r = r1 * n
k r)
let facCache,memoizedTRFactorial = memoize TRFactorialGuts
printfn "---"
let r = memoizedTRFactorial 4
printfn "%d" r
for KeyValue(k,v) in facCache do
printfn "%d: %d" k v
printfn "---"
let r2 = memoizedTRFactorial 5
printfn "%d" r2
printfn "---"
// comment out *** line, then run this
//let r3 = memoizedTRFactorial 100000
//printfn "%d" r3
let TRFibGuts n k memoGuts =
if n=0 || n=1 then
k 1
else
memoGuts (n-1) (fun r1 ->
memoGuts (n-2) (fun r2 ->
printfn "adding %d+%d" r1 r2 //%%%
let r = r1+r2
k r))
let fibCache, memoizedTRFib = memoize TRFibGuts
printfn "---"
let r5 = memoizedTRFib 4
printfn "%d" r5
for KeyValue(k,v) in fibCache do
printfn "%d: %d" k v
printfn "---"
let r6 = memoizedTRFib 5
printfn "%d" r6
printfn "---"
// comment out %%% line, then run this
//let r7 = memoizedTRFib 100000
//printfn "%d" r7
The predicament of memoizing tail-recursive functions is, of course, that when tail-recursive function
let f x =
......
f x1
calls itself, it is not allowed to do anything with a result of the recursive call, including putting it into cache. Tricky; so what can we do?
The critical insight here is that since the recursive function is not allowed to do anything with a result of recursive call, the result for all arguments to recursive calls will be the same! Therefore if recursion call trace is this
f x0 -> f x1 -> f x2 -> f x3 -> ... -> f xN -> res
then for all x in x0,x1,...,xN the result of f x will be the same, namely res. So the last invocation of a recursive function, the non-recursive call, knows the results for all the previous values - it is in a position to cache them. The only thing you need to do is to pass a list of visited values to it. Here is what it might look for factorial:
let cache = Dictionary<_,_>()
let rec fact0 l ((n,res) as arg) =
let commitToCache r =
l |> List.iter (fun a -> cache.Add(a,r))
match cache.TryGetValue(arg) with
| true, cachedResult -> commitToCache cachedResult; cachedResult
| false, _ ->
if n = 1 then
commitToCache res
cache.Add(arg, res)
res
else
fact0 (arg::l) (n-1, n*res)
let fact n = fact0 [] (n,1)
But wait! Look - l parameter of fact0 contains all the arguments to recursive calls to fact0 - just like the stack would in a non-tail-recursive version! That is exactly right. Any non-tail recursive algorithm can be converted to a tail-recursive one by moving the "list of stack frames" from stack to heap and converting the "postprocessing" of recursive call result into a walk over that data structure.
Pragmatic note: The factorial example above illustrates a general technique. It is quite useless as is - for factorial function it is quite enough to cache the top-level fact n result, because calculation of fact n for a particular n only hits a unique series of (n,res) pairs of arguments to fact0 - if (n,1) is not cached yet, then none of the pairs fact0 is going to be called on are.
Note that in this example, when we went from non-tail-recursive factorial to a tail-recursive factorial, we exploited the fact that multiplication is associative and commutative - tail-recursive factorial execute a different set of multiplications than a non-tail-recursive one.
In fact, a general technique exists for going from non-tail-recursive to tail-recursive algorithm, which yields an algorithm equivalent to a tee. This technique is called "continuatuion-passing transformation". Going that route, you can take a non-tail-recursive memoizing factorial and get a tail-recursive memoizing factorial by pretty much a mechanical transformation. See Brian's answer for exposition of this method.
I'm not sure if there's a simpler way to do this, but one approach would be to create a memoizing y-combinator:
let memoY f =
let cache = Dictionary<_,_>()
let rec fn x =
match cache.TryGetValue(x) with
| true,y -> y
| _ -> let v = f fn x
cache.Add(x,v)
v
fn
Then, you can use this combinator in lieu of "let rec", with the first argument representing the function to call recursively:
let tailRecFact =
let factHelper fact (x, res) =
printfn "%i,%i" x res
if x = 0 then res
else fact (x-1, x*res)
let memoized = memoY factHelper
fun x -> memoized (x,1)
EDIT
As Mitya pointed out, memoY doesn't preserve the tail recursive properties of the memoee. Here's a revised combinator which uses exceptions and mutable state to memoize any recursive function without overflowing the stack (even if the original function is not itself tail recursive!):
let memoY f =
let cache = Dictionary<_,_>()
fun x ->
let l = ResizeArray([x])
while l.Count <> 0 do
let v = l.[l.Count - 1]
if cache.ContainsKey(v) then l.RemoveAt(l.Count - 1)
else
try
cache.[v] <- f (fun x ->
if cache.ContainsKey(x) then cache.[x]
else
l.Add(x)
failwith "Need to recurse") v
with _ -> ()
cache.[x]
Unfortunately, the machinery which is inserted into each recursive call is somewhat heavy, so performance on un-memoized inputs requiring deep recursion can be a bit slow. However, compared to some other solutions, this has the benefit that it requires fairly minimal changes to the natural expression of recursive functions:
let fib = memoY (fun fib n ->
printfn "%i" n;
if n <= 1 then n
else (fib (n-1)) + (fib (n-2)))
let _ = fib 5000
EDIT
I'll expand a bit on how this compares to other solutions. This technique takes advantage of the fact that exceptions provide a side channel: a function of type 'a -> 'b doesn't actually need to return a value of type 'b, but can instead exit via an exception. We wouldn't need to use exceptions if the return type explicitly contained an additional value indicating failure. Of course, we could use the 'b option as the return type of the function for this purpose. This would lead to the following memoizing combinator:
let memoO f =
let cache = Dictionary<_,_>()
fun x ->
let l = ResizeArray([x])
while l.Count <> 0 do
let v = l.[l.Count - 1]
if cache.ContainsKey v then l.RemoveAt(l.Count - 1)
else
match f(fun x -> if cache.ContainsKey x then Some(cache.[x]) else l.Add(x); None) v with
| Some(r) -> cache.[v] <- r;
| None -> ()
cache.[x]
Previously, our memoization process looked like:
fun fib n ->
printfn "%i" n;
if n <= 1 then n
else (fib (n-1)) + (fib (n-2))
|> memoY
Now, we need to incorporate the fact that fib should return an int option instead of an int. Given a suitable workflow for option types, this could be written as follows:
fun fib n -> option {
printfn "%i" n
if n <= 1 then return n
else
let! x = fib (n-1)
let! y = fib (n-2)
return x + y
} |> memoO
However, if we're willing to change the return type of the first parameter (from int to int option in this case), we may as well go all the way and just use continuations in the return type instead, as in Brian's solution. Here's a variation on his definitions:
let memoC f =
let cache = Dictionary<_,_>()
let rec fn n k =
match cache.TryGetValue(n) with
| true, r -> k r
| _ ->
f fn n (fun r ->
cache.Add(n,r)
k r)
fun n -> fn n id
And again, if we have a suitable computation expression for building CPS functions, we can define our recursive function like this:
fun fib n -> cps {
printfn "%i" n
if n <= 1 then return n
else
let! x = fib (n-1)
let! y = fib (n-2)
return x + y
} |> memoC
This is exactly the same as what Brian has done, but I find the syntax here is easier to follow. To make this work, all we need are the following two definitions:
type CpsBuilder() =
member this.Return x k = k x
member this.Bind(m,f) k = m (fun a -> f a k)
let cps = CpsBuilder()
I wrote a test to visualize the memoization. Each dot is a recursive call.
......720 // factorial 6
......720 // factorial 6
.....120 // factorial 5
......720 // memoizedFactorial 6
720 // memoizedFactorial 6
120 // memoizedFactorial 5
......720 // tailRecFact 6
720 // tailRecFact 6
.....120 // tailRecFact 5
......720 // tailRecursiveMemoizedFactorial 6
720 // tailRecursiveMemoizedFactorial 6
.....120 // tailRecursiveMemoizedFactorial 5
kvb's solution returns the same results are straight memoization like this function.
let tailRecursiveMemoizedFactorial =
memoize
(fun x ->
let rec factorialUtil x res =
if x = 0 then
res
else
printf "."
let newRes = x * res
factorialUtil (x - 1) newRes
factorialUtil x 1
)
Test source code.
open System.Collections.Generic
let memoize f =
let cache = new Dictionary<_, _>()
(fun x ->
match cache.TryGetValue(x) with
| true, y -> y
| _ ->
let v = f(x)
cache.Add(x, v)
v)
let rec factorial(x) =
if (x = 0) then
1
else
printf "."
x * factorial(x - 1)
let rec memoizedFactorial =
memoize (
fun x ->
if (x = 0) then
1
else
printf "."
x * memoizedFactorial(x - 1))
let memoY f =
let cache = Dictionary<_,_>()
let rec fn x =
match cache.TryGetValue(x) with
| true,y -> y
| _ -> let v = f fn x
cache.Add(x,v)
v
fn
let tailRecFact =
let factHelper fact (x, res) =
if x = 0 then
res
else
printf "."
fact (x-1, x*res)
let memoized = memoY factHelper
fun x -> memoized (x,1)
let tailRecursiveMemoizedFactorial =
memoize
(fun x ->
let rec factorialUtil x res =
if x = 0 then
res
else
printf "."
let newRes = x * res
factorialUtil (x - 1) newRes
factorialUtil x 1
)
factorial 6 |> printfn "%A"
factorial 6 |> printfn "%A"
factorial 5 |> printfn "%A\n"
memoizedFactorial 6 |> printfn "%A"
memoizedFactorial 6 |> printfn "%A"
memoizedFactorial 5 |> printfn "%A\n"
tailRecFact 6 |> printfn "%A"
tailRecFact 6 |> printfn "%A"
tailRecFact 5 |> printfn "%A\n"
tailRecursiveMemoizedFactorial 6 |> printfn "%A"
tailRecursiveMemoizedFactorial 6 |> printfn "%A"
tailRecursiveMemoizedFactorial 5 |> printfn "%A\n"
System.Console.ReadLine() |> ignore
That should work if mutual tail recursion through y are not creating stack frames:
let rec y f x = f (y f) x
let memoize (d:System.Collections.Generic.Dictionary<_,_>) f n =
if d.ContainsKey n then d.[n]
else d.Add(n, f n);d.[n]
let rec factorialucps factorial' n cont =
if n = 0I then cont(1I) else factorial' (n-1I) (fun k -> cont (n*k))
let factorialdpcps =
let d = System.Collections.Generic.Dictionary<_, _>()
fun n -> y (factorialucps >> fun f n -> memoize d f n ) n id
factorialdpcps 15I //1307674368000
I write some code to learning F#.
Here is a example:
let nextPrime list=
let rec loop n=
match n with
| _ when (list |> List.filter (fun x -> x <= ( n |> double |> sqrt |> int)) |> List.forall (fun x -> n % x <> 0)) -> n
| _ -> loop (n+1)
loop (List.max list + 1)
let rec findPrimes num=
match num with
| 1 -> [2]
| n ->
let temp = findPrimes <| n-1
(nextPrime temp ) :: temp
//find 10 primes
findPrimes 10 |> printfn "%A"
I'm very happy that it just works!
I'm totally beginner to recursion
Recursion is a wonderful thing.
I think findPrimes is not efficient.
Someone help me to refactor findPrimes to tail recursion if possible?
BTW, is there some more efficient way to find first n primes?
Regarding the first part of your question, if you want to write a recursive list building function tail-recursively you should pass the list of intermediate results as an extra parameter to the function. In your case this would be something like
let findPrimesTailRecursive num =
let rec aux acc num =
match num with
| 1 -> acc
| n -> aux ((nextPrime acc)::acc) (n-1)
aux [2] num
The recursive function aux gathers its results in an extra parameter conveniently called acc (as in acc-umulator). When you reach your ending condition, just spit out the accumulated result. I've wrapped the tail-recursive helper function in another function, so the function signature remains the same.
As you can see, the call to aux is the only, and therefore last, call to happen in the n <> 1 case. It's now tail-recursive and will compile into a while loop.
I've timed your version and mine, generating 2000 primes. My version is 16% faster, but still rather slow. For generating primes, I like to use an imperative array sieve. Not very functional, but very (very) fast.
An alternative is to use an extra continuation argument to make findPrimes tail recursive. This technique always works. It will avoid stack overflows, but probably won't make your code faster.
Also, I put your nextPrime function a little closer to the style I'd use.
let nextPrime list=
let rec loop n = if list |> List.filter (fun x -> x*x <= n)
|> List.forall (fun x -> n % x <> 0)
then n
else loop (n+1)
loop (1 + List.head list)
let rec findPrimesC num cont =
match num with
| 1 -> cont [2]
| n -> findPrimesC (n-1) (fun temp -> nextPrime temp :: temp |> cont)
let findPrimes num = findPrimesC num (fun res -> res)
findPrimes 10
As others have said, there's faster ways to generate primes.
Why not simply write:
let isPrime n =
if n<=1 then false
else
let m = int(sqrt (float(n)))
{2..m} |> Seq.forall (fun i->n%i<>0)
let findPrimes n =
{2..n} |> Seq.filter isPrime |> Seq.toList
or sieve (very fast):
let generatePrimes max=
let p = Array.create (max+1) true
let rec filter i step =
if i <= max then
p.[i] <- false
filter (i+step) step
{2..int (sqrt (float max))} |> Seq.iter (fun i->filter (i+i) i)
{2..max} |> Seq.filter (fun i->p.[i]) |> Seq.toArray
BTW, is there some more efficient way to find first n primes?
I described a fast arbitrary-size Sieve of Eratosthenes in F# here that accumulated its results into an ever-growing ResizeArray:
> let primes =
let a = ResizeArray[2]
let grow() =
let p0 = a.[a.Count-1]+1
let b = Array.create p0 true
for di in a do
let rec loop i =
if i<b.Length then
b.[i] <- false
loop(i+di)
let i0 = p0/di*di
loop(if i0<p0 then i0+di-p0 else i0-p0)
for i=0 to b.Length-1 do
if b.[i] then a.Add(p0+i)
fun n ->
while n >= a.Count do
grow()
a.[n];;
val primes : (int -> int)
I know that this is a bit late, and an answer was already accepted. However, I believe that a good step by step guide to making something tail recursive may be of interest to the OP or anyone else for that matter. Here are some tips that have certainly helped me out. I'm going to use a strait-forward example other than prime generation because, as others have stated, there are better ways to generate primes.
Consider a naive implementation of a count function that will create a list of integers counting down from some n. This version is not tail recursive so for long lists you will encounter a stack overflow exception:
let rec countDown = function
| 0 -> []
| n -> n :: countDown (n - 1)
(* ^
|... the cons operator is in the tail position
as such it is evaluated last. this drags
stack frames through subsequent recursive
calls *)
One way to fix this is to apply continuation passing style with a parameterized function:
let countDown' n =
let rec countDown n k =
match n with
| 0 -> k [] (* v--- this is continuation passing style *)
| n -> countDown (n - 1) (fun ns -> n :: k ns)
(* ^
|... the recursive call is now in tail position *)
countDown n (fun ns -> ns)
(* ^
|... and we initialize k with the identity function *)
Then, refactor this parameterized function into a specialized representation. Notice that the function countDown' is not actually counting down. This is an artifact of the way the continuation is built up when n > 0 and then evaluated when n = 0. If you have something like the first example and you can't figure out how to make it tail recursive, what I'm suggesting is that you write the second one and then try to optimize it to eliminate the function parameter k. That will certainly improve the readability. This is an optimization of the second example:
let countDown'' n =
let rec countDown n ns =
match n with
| 0 -> List.rev ns (* reverse so we are actually counting down again *)
| n -> countDown (n - 1) (n :: ns)
countDown n []
I'm new to functional world and appreciate help on this one.
I want to SUPERCEDE ugly imperative code from this simple function, but don't know how to do it.
What I want is to randomly pick some element from IEnumerable (seq in F#) with a respect to probability value - second item in tuple (so item with "probability" 0.7 will be picked more often than with 0.1).
/// seq<string * float>
let probabilitySeq = seq [ ("a", 0.7); ("b", 0.6); ("c", 0.5); ("d", 0.1) ]
/// seq<'a * float> -> 'a
let randomPick probSeq =
let sum = Seq.fold (fun s dir -> s + snd dir) 0.0 probSeq
let random = (new Random()).NextDouble() * sum
// vvvvvv UGLY vvvvvv
let mutable count = random
let mutable ret = fst (Seq.hd probSeq )
let mutable found = false
for item in probSeq do
count <- count - snd item
if (not found && (count < 0.0)) then
ret <- fst item //return ret; //in C#
found <- true
// ^^^^^^ UGLY ^^^^^^
ret
////////// at FSI: //////////
> randomPick probabilitySeq;;
val it : string = "a"
> randomPick probabilitySeq;;
val it : string = "c"
> randomPick probabilitySeq;;
val it : string = "a"
> randomPick probabilitySeq;;
val it : string = "b"
I think that randomPick is pretty straightforward to implement imperatively, but functionally?
This is functional, but take list not seq (wanted).
//('a * float) list -> 'a
let randomPick probList =
let sum = Seq.fold (fun s dir -> s + snd dir) 0.0 probList
let random = (new Random()).NextDouble() * sum
let rec pick_aux p list =
match p, list with
| gt, h::t when gt >= snd h -> pick_aux (p - snd h) t
| lt, h::t when lt < snd h -> fst h
| _, _ -> failwith "Some error"
pick_aux random probList
An F# solution using the principle suggested by Matajon:
let randomPick probList =
let ps = Seq.skip 1 (Seq.scan (+) 0.0 (Seq.map snd probList))
let random = (new Random()).NextDouble() * (Seq.fold (fun acc e -> e) 0.0 ps)
Seq.find (fun (p, e) -> p >= random)
(Seq.zip ps (Seq.map fst probList))
|> snd
But I would probably also use a list-based approach in this case since the sum of the probability values needs to be precalculated anyhow...
I will provide only Haskell version since I don't have F# present on my notebook, it should be similar. The principle is to convert your sequence to sequence like
[(0.7,"a"),(1.3,"b"),(1.8,"c"),(1.9,"d")]
where each first element in the tuple is representing not probablity but something like range. Then it is easy, pick one random number from 0 to last number (1.9) and check in which range it belongs to. For example if 0.5 is chosen, it will be "a" because 0.5 is lower than 0.7.
Haskell code -
probabilitySeq = [("a", 0.7), ("b", 0.6), ("c", 0.5), ("d", 0.1)]
modifySeq :: [(String, Double)] -> [(Double, String)]
modifySeq seq = modifyFunction 0 seq where
modifyFunction (_) [] = []
modifyFunction (acc) ((a, b):xs) = (acc + b, a) : modifyFunction (acc + b) xs
pickOne :: [(Double, String)] -> IO String
pickOne seq = let max = (fst . last) seq in
do
random <- randomRIO (0, max)
return $ snd $ head $ dropWhile (\(a, b) -> a < random) seq
result :: [(String, Double)] -> IO String
result = pickOne . modifySeq
Example -
*Main> result probabilitySeq
"b"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"d"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"b"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"c"
*Main> result probabilitySeq
"a"
*Main> result probabilitySeq
"c"
The way I understand it, you're logic works like this:
Sum all the weights, then select a random double somewhere between 0 and the sum of all the weights. Find the item which corresponds to your probability.
In other words, you want to map your list as follows:
Item Val Offset Max (Val + Offset)
---- --- ------ ------------------
a 0.7 0.0 0.7
b 0.6 0.7 1.3
c 0.5 1.3 1.8
d 0.1 1.8 1.9
Transforming a list of (item, probability) to (item, max) is straightforward:
let probabilityMapped prob =
[
let offset = ref 0.0
for (item, probability) in prob do
yield (item, probability + !offset)
offset := !offset + probability
]
Although this falls back on mutables, its pure, deterministic, and in the spirit of readable code. If you insist on avoiding mutable state, you can use this (not tail-recursive):
let probabilityMapped prob =
let rec loop offset = function
| [] -> []
| (item, prob)::xs -> (item, prob + offset)::loop (prob + offset) xs
loop 0.0 prob
Although we're threading state through the list, we're performing a map, not a fold operation, so we shouldn't use the Seq.fold or Seq.scan methods. I started writing code using Seq.scan, and it looked hacky and strange.
Whatever method you choose, once you get your list mapped, its very easy to select a randomly weighted item in linear time:
let rnd = new System.Random()
let randomPick probSeq =
let probMap =
[
let offset = ref 0.0
for (item, probability) in probSeq do
yield (item, probability + !offset)
offset := !offset + probability
]
let max = Seq.maxBy snd probMap |> snd
let rndNumber = rnd.NextDouble() * max
Seq.pick (fun (item, prob) -> if rndNumber <= prob then Some(item) else None) probMap
I would use Seq.to_list to transform the input sequence into a list and then use the list based approach. The list quoted is short enough that it shouldn't be an unreasonable overhead.
The simplest solution is to use ref to store state between calls to iterator for any suitable function from Seq module:
let probabilitySeq = seq [ ("a", 0.7); ("b", 0.6); ("c", 0.5); ("d", 0.1) ]
let randomPick probSeq =
let sum = Seq.fold (fun s (_,v) -> s + v) 0.0 probSeq
let random = ref (System.Random().NextDouble() * sum)
let aux = function
| _,v when !random >= v ->
random := !random - v
None
| s,_ -> Some s
match Seq.first aux probSeq with
| Some r -> r
| _ -> fst (Seq.hd probSeq)
I would use your functional, list-based version, but adapt it to use LazyList from the F# PowerPack. Using LazyList.of_seq will give you the moral equivalent of a list, but without evaluating the whole thing at once. You can even pattern match on LazyLists with the LazyList.(|Cons|Nil|) pattern.
I think that cfern's suggestion is actually simplest (?= best) solution to this.
Entire input needs to be evaluated, so seq's advantage of yield-on-demand is lost anyway. Easiest seems to take sequence as input and convert it to a list and total sum at the same time. Then use the list for the list-based portion of the algorithm (list will be in reverse order, but that doesn't matter for the calculation).
let randomPick moveList =
let sum, L = moveList
|> Seq.fold (fun (sum, L) dir -> sum + snd dir, dir::L) (0.0, [])
let rec pick_aux p list =
match p, list with
| gt, h::t when gt >= snd h -> pick_aux (p - snd h) t
| lt, h::t when lt < snd h -> fst h
| _, _ -> failwith "Some error"
pick_aux (rand.NextDouble() * sum) L
Thanks for Yours solutions, especially Juliet and Johan (I've to read it few times to actually get it).
:-)