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Why is the following function returning a sequence of incorrect length when the repl variable is set to false?
open MathNet.Numerics.Distributions
open MathNet.Numerics.LinearAlgebra
let sample (data : seq<float>) (size : int) (repl : bool) =
let n = data |> Seq.length
// without replacement
let rec generateIndex idx =
let m = size - Seq.length(idx)
match m > 0 with
| true ->
let newIdx = DiscreteUniform.Samples(0, n-1) |> Seq.take m
let idx = (Seq.append idx newIdx) |> Seq.distinct
generateIndex idx
| false ->
idx
let sample =
match repl with
| true ->
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> Seq.item index data)
| false ->
generateIndex (seq [])
|> Seq.map (fun index -> Seq.item index data)
sample
Running the function...
let requested = 1000
let dat = Normal.Samples(0., 1.) |> Seq.take 10000
let resultlen = sample dat requested false |> Seq.length
printfn "requested -> %A\nreturned -> %A" requested resultlen
Resulting lengths are wrong.
>
requested -> 1000
returned -> 998
>
requested -> 1000
returned -> 1001
>
requested -> 1000
returned -> 997
Any idea what mistake I'm making?
First, there's a comment I want to make about coding style. Then I'll get to the explanation of why your sequences are coming back with different lengths.
In the comments, I mentioned replacing match (bool) with true -> ... | false -> ... with a simple if ... then ... else expression, but there's another coding style that you're using that I think could be improved. You wrote:
let sample (various_parameters) = // This is a function
// Other code ...
let sample = some_calculation // This is a variable
sample // Return the variable
While F# allows you to reuse names like that, and the name inside the function will "shadow" the name outside the function, it's generally a bad idea for the reused name to have a totally different type than the original name. In other words, this can be a good idea:
let f (a : float option) =
let a = match a with
| None -> 0.0
| Some value -> value
// Now proceed, knowing that `a` has a real value even if had been None before
Or, because the above is exactly what F# gives you defaultArg for:
let f (a : float option) =
let a = defaultArg a 0.0
// This does exactly the same thing as the previous snippet
Here, we are making the name a inside our function refer to a different type than the parameter named a: the parameter was a float option, and the a inside our function is a float. But they're sort of the "same" type -- that is, there's very little mental difference between "The caller may have specified a floating-point value or they may not" and "Now I definitely have a floating-point value". But there's a very large mental gap between "The name sample is a function that takes three parameters" and "The name sample is a sequence of floats". I strongly recommend using a name like result for the value you're going to return from your function, rather than re-using the function name.
Also, this seems unnecessarily verbose:
let result =
match repl with
| true ->
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> Seq.item index data)
| false ->
generateIndex (seq [])
|> Seq.map (fun index -> Seq.item index data)
result
Anytime I find myself writing "let result = (something) ; result" at the end of my function, I usually just want to replace that whole code block with just the (something). I.e., the above snippet could just become:
match repl with
| true ->
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> Seq.item index data)
| false ->
generateIndex (seq [])
|> Seq.map (fun index -> Seq.item index data)
Which in turn can be replaced with an if...then...else expression:
if repl then
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> Seq.item index data)
else
generateIndex (seq [])
|> Seq.map (fun index -> Seq.item index data)
And that's the last expression in your code. In other words, I would probably rewrite your function as follows (changing ONLY the style, and making no changes to the logic):
open MathNet.Numerics.Distributions
open MathNet.Numerics.LinearAlgebra
let sample (data : seq<float>) (size : int) (repl : bool) =
let n = data |> Seq.length
// without replacement
let rec generateIndex idx =
let m = size - Seq.length(idx)
if m > 0 then
let newIdx = DiscreteUniform.Samples(0, n-1) |> Seq.take m
let idx = (Seq.append idx newIdx) |> Seq.distinct
generateIndex idx
else
idx
if repl then
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> Seq.item index data)
else
generateIndex (seq [])
|> Seq.map (fun index -> Seq.item index data)
If I can figure out why your sequences have the wrong length, I'll update this answer with that information as well.
UPDATE: Okay, I think I see what's happening in your generateIndex function that's giving you unexpected results. There are two things tripping you up: one is sequence laziness, and the other is randomness.
I copied your generateIndex function into VS Code and added some printfn statements to look at what's going on. First, the code I ran, and then the results:
let rec generateIndex n size idx =
let m = size - Seq.length(idx)
printfn "m = %d" m
match m > 0 with
| true ->
let newIdx = DiscreteUniform.Samples(0, n-1) |> Seq.take m
printfn "Generating newIdx as %A" (List.ofSeq newIdx)
let idx = (Seq.append idx newIdx) |> Seq.distinct
printfn "Now idx is %A" (List.ofSeq idx)
generateIndex n size idx
| false ->
printfn "Done, returning %A" (List.ofSeq idx)
idx
All those List.ofSeq idx calls are so that F# Interactive would print more than four items of the seq when I print it out (by default, if you try to print a seq with %A, it will only print out four values and then print an ellipsis if there are more values available in the seq). Also, I turned n and size into parameters (that I don't change between calls) so that I could test it easily. I then called it as generateIndex 100 5 (seq []) and got the following result:
m = 5
Generating newIdx as [74; 76; 97; 78; 31]
Now idx is [68; 28; 65; 58; 82]
m = 0
Done, returning [37; 58; 24; 48; 49]
val it : seq<int> = seq [12; 69; 97; 38; ...]
See how the numbers keep changing? That was my first clue that something was up. See, seqs are lazy. They don't evaluate their contents until they have to. You shouldn't think of a seq as a list of numbers. Instead, think of it as a generator that will, when asked for numbers, produce them according to some rule. In your case, the rule is "Choose random integers between 0 and n-1, then take m of those numbers". And the other thing about seqs is that they do not cache their contents (although there's a Seq.cache function available that will cache their contents). Therefore, if you have a seq based on a random number generator, its results will be different each time, as you can see in my output. When I printed out newIdx, it printed out as [74; 76; 97; 78; 31], but when I appended it to an empty seq, the result printed out as [68; 28; 65; 58; 82].
Why this difference? Because Seq.append does not force evaluation. It simply creates a new seq whose rule is "take all items from the first seq, then when that one exhausts, take all items from the second seq. And when that one exhausts, end." And Seq.distinct does not force evaluation either; it simply creates a new seq whose rule is "take the items from the seq handed to you, and start handing them out when asked. But memorize them as you go, and if you've handed one of them out before, don't hand it out again." So what you are passing around between your calls to generateIdx is an object that, when evaluated, will pick a set of random numbers between 0 and n-1 (in my simple case, between 0 and 100) and then reduce that set down to a distinct set of numbers.
Now, here's the thing. Every time you evaluate that seq, it will start from the beginning: first calling DiscreteUniform.Samples(0, n-1) to generate an infinite stream of random numbers, then selecting m numbers from that stream, then throwing out any duplicates. (I'm ignoring the Seq.append for now, because it would create unnecessary mental complexity and it isn't really part of the bug anyway). Now, at the start of each go-round of your function, you check the length of the sequence, which does cause it to be evaluated. That means that it selects (in the case of my sample code) 5 random numbers between 0 and 99, then makes sure that they're all distinct. If they are all distinct, then m = 0 and the function will exit, returning... not the list of numbers, but the seq object. And when that seq object is evaluated, it will start over from the beginning, choosing a different set of 5 random numbers and then throwing out any duplicates. Therefore, there's still a chance that at least one of that set of 5 numbers will end up being a duplicate, because the sequence whose length was tested (which we know contained no duplicates, otherwise m would have been greater than 0) was not the sequence that was returned. The sequence that was returned has a 1.0 * 0.99 * 0.98 * 0.97 * 0.96 chance of not containing any duplicates, which comes to about 0.9035. So there's a just-under-10% chance that even though you checked Seq.length and it was 5, the length of the returned seq ends up being 4 after all -- because it was choosing a different set of random numbers than the one you checked.
To prove this, I ran the function again, this time only picking 4 numbers so that the result would be completely shown at the F# Interactive prompt. And my run of generateIndex 100 4 (seq []) produced the following output:
m = 4
Generating newIdx as [36; 63; 97; 31]
Now idx is [39; 93; 53; 94]
m = 0
Done, returning [47; 94; 34]
val it : seq<int> = seq [48; 24; 14; 68]
Notice how when I printed "Done, returning (value of idx)", it had only 3 values? Even though it eventually returned 4 values (because it picked a different selection of random numbers for the actual result, and that selection had no duplicates), that demonstrated the problem.
By the way, there's one other problem with your function, which is that it's far slower than it needs to be. The function Seq.item, in some circumstances, has to run through the sequence from the beginning in order to pick the nth item of the sequence. It would be far better to store your data in an array at the start of your function (let arrData = data |> Array.ofSeq), then replace
|> Seq.map (fun index -> Seq.item index data)
with
|> Seq.map (fun index -> arrData.[index])
Array lookups are done in constant time, so that takes your sample function down from O(N^2) to O(N).
TL;DR: Use Seq.distinct before you take m values from it and the bug will go away. You can just replace your entire generateIdx function with a simple DiscreteUniform.Samples(0, n-1) |> Seq.distinct |> Seq.take size. (And use an array for your data lookups so that your function will run faster). In other words, here's the final almost-final version of how I would rewrite your code:
let sample (data : seq<float>) (size : int) (repl : bool) =
let arrData = data |> Array.ofSeq
let n = arrData |> Array.length
if repl then
DiscreteUniform.Samples(0, n-1)
|> Seq.take size
|> Seq.map (fun index -> arrData.[index])
else
DiscreteUniform.Samples(0, n-1)
|> Seq.distinct
|> Seq.take size
|> Seq.map (fun index -> arrData.[index])
That's it! Simple, easy to understand, and (as far as I can tell) bug-free.
Edit: ... but not completely DRY, because there's still a bit of repeated code in that "final" version. (Credit to CaringDev for pointing it out in the comments below). The Seq.take size |> Seq.map is repeated in both branches of the if expression, so there's a way to simplify that expression. We could do this:
let randomIndices =
if repl then
DiscreteUniform.Samples(0, n-1)
else
DiscreteUniform.Samples(0, n-1) |> Seq.distinct
randomIndices
|> Seq.take size
|> Seq.map (fun index -> arrData.[index])
So here's a truly-final version of my suggestion:
let sample (data : seq<float>) (size : int) (repl : bool) =
let arrData = data |> Array.ofSeq
let n = arrData |> Array.length
let randomIndices =
if repl then
DiscreteUniform.Samples(0, n-1)
else
DiscreteUniform.Samples(0, n-1) |> Seq.distinct
randomIndices
|> Seq.take size
|> Seq.map (fun index -> arrData.[index])
I'm writting some generators and an Arbitrary, but is too slow (see the GC numbers also). I think I have an error on my code, but I can't figure out where. Or my approach (map2 (fold)) is "weird"?.
Generators:
type Generators () =
static let notAllowed = Array.append [| '0'..'9' |] [| '\n'; '\r'; '['; ']'; '/'; |]
static let containsInvalidValues (s : string) = s.IndexOfAny(notAllowed) <> -1
static member positiveIntsGen() = Arb.generate<PositiveInt> |> Gen.map int
static member separatorStringGen() =
Arb.generate<NonEmptyString>
|> Gen.suchThat (fun s -> s.Get.Length < 5 && not (s.Get |> containsInvalidValues))
Arbitrary:
let manyNumbersNewLineCustomDelimiterStrInput =
Gen.map2 (fun (ints : int[]) (nes : NonEmptyString) ->
Array.fold (fun acc num ->
if num % 2 = 0 then acc + "," + num.ToString()
else if num % 3 = 0 then acc + "\n" + num.ToString()
else acc + "\n" + num.ToString()) ("//[" + nes.Get + "]\n") ints )
(Generators.array12OfIntsGen())
(Generators.separatorStringGen())
|> Arb.fromGen
The configuration have the MaxTest = 500 and it takes ~5 minutes to complete.
Output (using #timer):
StrCalcTest.get_When pass an string that starts with "//[" and contains "]\n" use the multicharacter value between them as separator-Ok, passed 500 tests.
Real: 00:07:03.467, CPU: 00:07:03.296, GC gen0: 75844, gen1: 71968, gen2: 4
Without actually testing anything, my guess would be that the problematic part is this:
Arb.generate<NonEmptyString>
|> Gen.suchThat (fun s -> s.Get.Length < 5 && not (s.Get |> containsInvalidValues))
This means that you will generate strings and filtering out all those that satisfy certain conditions. But if the conditions are too restrictive, FsCheck might need to generate a very large number of strings until you actually get some that pass the test.
If you can express the rule so that you are generating the strings so that everything you generate is a valid string, then I think it should be faster.
Could you, for example, generate a number n (for the string length) followed by n values of type char (that satisfy your conditions) and then append them to form the separator string? (I think FsCheck's gen { .. } computation might be a nice way of writing that.)
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).
:-)
Background:
I have a sequence of contiguous, time-stamped data. The data-sequence has gaps in it where the data is not contiguous. I want create a method to split the sequence up into a sequence of sequences so that each subsequence contains contiguous data (split the input-sequence at the gaps).
Constraints:
The return value must be a sequence of sequences to ensure that elements are only produced as needed (cannot use list/array/cacheing)
The solution must NOT be O(n^2), probably ruling out a Seq.take - Seq.skip pattern (cf. Brian's post)
Bonus points for a functionally idiomatic approach (since I want to become more proficient at functional programming), but it's not a requirement.
Method signature
let groupContiguousDataPoints (timeBetweenContiguousDataPoints : TimeSpan) (dataPointsWithHoles : seq<DateTime * float>) : (seq<seq< DateTime * float >>)= ...
On the face of it the problem looked trivial to me, but even employing Seq.pairwise, IEnumerator<_>, sequence comprehensions and yield statements, the solution eludes me. I am sure that this is because I still lack experience with combining F#-idioms, or possibly because there are some language-constructs that I have not yet been exposed to.
// Test data
let numbers = {1.0..1000.0}
let baseTime = DateTime.Now
let contiguousTimeStamps = seq { for n in numbers ->baseTime.AddMinutes(n)}
let dataWithOccationalHoles = Seq.zip contiguousTimeStamps numbers |> Seq.filter (fun (dateTime, num) -> num % 77.0 <> 0.0) // Has a gap in the data every 77 items
let timeBetweenContiguousValues = (new TimeSpan(0,1,0))
dataWithOccationalHoles |> groupContiguousDataPoints timeBetweenContiguousValues |> Seq.iteri (fun i sequence -> printfn "Group %d has %d data-points: Head: %f" i (Seq.length sequence) (snd(Seq.hd sequence)))
I think this does what you want
dataWithOccationalHoles
|> Seq.pairwise
|> Seq.map(fun ((time1,elem1),(time2,elem2)) -> if time2-time1 = timeBetweenContiguousValues then 0, ((time1,elem1),(time2,elem2)) else 1, ((time1,elem1),(time2,elem2)) )
|> Seq.scan(fun (indexres,(t1,e1),(t2,e2)) (index,((time1,elem1),(time2,elem2))) -> (index+indexres,(time1,elem1),(time2,elem2)) ) (0,(baseTime,-1.0),(baseTime,-1.0))
|> Seq.map( fun (index,(time1,elem1),(time2,elem2)) -> index,(time2,elem2) )
|> Seq.filter( fun (_,(_,elem)) -> elem <> -1.0)
|> PSeq.groupBy(fst)
|> Seq.map(snd>>Seq.map(snd))
Thanks for asking this cool question
I translated Alexey's Haskell to F#, but it's not pretty in F#, and still one element too eager.
I expect there is a better way, but I'll have to try again later.
let N = 20
let data = // produce some arbitrary data with holes
seq {
for x in 1..N do
if x % 4 <> 0 && x % 7 <> 0 then
printfn "producing %d" x
yield x
}
let rec GroupBy comp (input:LazyList<'a>) : LazyList<LazyList<'a>> =
LazyList.delayed (fun () ->
match input with
| LazyList.Nil -> LazyList.cons (LazyList.empty()) (LazyList.empty())
| LazyList.Cons(x,LazyList.Nil) ->
LazyList.cons (LazyList.cons x (LazyList.empty())) (LazyList.empty())
| LazyList.Cons(x,(LazyList.Cons(y,_) as xs)) ->
let groups = GroupBy comp xs
if comp x y then
LazyList.consf
(LazyList.consf x (fun () ->
let (LazyList.Cons(firstGroup,_)) = groups
firstGroup))
(fun () ->
let (LazyList.Cons(_,otherGroups)) = groups
otherGroups)
else
LazyList.cons (LazyList.cons x (LazyList.empty())) groups)
let result = data |> LazyList.of_seq |> GroupBy (fun x y -> y = x + 1)
printfn "Consuming..."
for group in result do
printfn "about to do a group"
for x in group do
printfn " %d" x
You seem to want a function that has signature
(`a -> bool) -> seq<'a> -> seq<seq<'a>>
I.e. a function and a sequence, then break up the input sequence into a sequence of sequences based on the result of the function.
Caching the values into a collection that implements IEnumerable would likely be simplest (albeit not exactly purist, but avoiding iterating the input multiple times. It will lose much of the laziness of the input):
let groupBy (fun: 'a -> bool) (input: seq) =
seq {
let cache = ref (new System.Collections.Generic.List())
for e in input do
(!cache).Add(e)
if not (fun e) then
yield !cache
cache := new System.Collections.Generic.List()
if cache.Length > 0 then
yield !cache
}
An alternative implementation could pass cache collection (as seq<'a>) to the function so it can see multiple elements to chose the break points.
A Haskell solution, because I don't know F# syntax well, but it should be easy enough to translate:
type TimeStamp = Integer -- ticks
type TimeSpan = Integer -- difference between TimeStamps
groupContiguousDataPoints :: TimeSpan -> [(TimeStamp, a)] -> [[(TimeStamp, a)]]
There is a function groupBy :: (a -> a -> Bool) -> [a] -> [[a]] in the Prelude:
The group function takes a list and returns a list of lists such that the concatenation of the result is equal to the argument. Moreover, each sublist in the result contains only equal elements. For example,
group "Mississippi" = ["M","i","ss","i","ss","i","pp","i"]
It is a special case of groupBy, which allows the programmer to supply their own equality test.
It isn't quite what we want, because it compares each element in the list with the first element of the current group, and we need to compare consecutive elements. If we had such a function groupBy1, we could write groupContiguousDataPoints easily:
groupContiguousDataPoints maxTimeDiff list = groupBy1 (\(t1, _) (t2, _) -> t2 - t1 <= maxTimeDiff) list
So let's write it!
groupBy1 :: (a -> a -> Bool) -> [a] -> [[a]]
groupBy1 _ [] = [[]]
groupBy1 _ [x] = [[x]]
groupBy1 comp (x : xs#(y : _))
| comp x y = (x : firstGroup) : otherGroups
| otherwise = [x] : groups
where groups#(firstGroup : otherGroups) = groupBy1 comp xs
UPDATE: it looks like F# doesn't let you pattern match on seq, so it isn't too easy to translate after all. However, this thread on HubFS shows a way to pattern match sequences by converting them to LazyList when needed.
UPDATE2: Haskell lists are lazy and generated as needed, so they correspond to F#'s LazyList (not to seq, because the generated data is cached (and garbage collected, of course, if you no longer hold a reference to it)).
(EDIT: This suffers from a similar problem to Brian's solution, in that iterating the outer sequence without iterating over each inner sequence will mess things up badly!)
Here's a solution that nests sequence expressions. The imperitave nature of .NET's IEnumerable<T> is pretty apparent here, which makes it a bit harder to write idiomatic F# code for this problem, but hopefully it's still clear what's going on.
let groupBy cmp (sq:seq<_>) =
let en = sq.GetEnumerator()
let rec partitions (first:option<_>) =
seq {
match first with
| Some first' -> //'
(* The following value is always overwritten;
it represents the first element of the next subsequence to output, if any *)
let next = ref None
(* This function generates a subsequence to output,
setting next appropriately as it goes *)
let rec iter item =
seq {
yield item
if (en.MoveNext()) then
let curr = en.Current
if (cmp item curr) then
yield! iter curr
else // consumed one too many - pass it on as the start of the next sequence
next := Some curr
else
next := None
}
yield iter first' (* ' generate the first sequence *)
yield! partitions !next (* recursively generate all remaining sequences *)
| None -> () // return an empty sequence if there are no more values
}
let first = if en.MoveNext() then Some en.Current else None
partitions first
let groupContiguousDataPoints (time:TimeSpan) : (seq<DateTime*_> -> _) =
groupBy (fun (t,_) (t',_) -> t' - t <= time)
Okay, trying again. Achieving the optimal amount of laziness turns out to be a bit difficult in F#... On the bright side, this is somewhat more functional than my last attempt, in that it doesn't use any ref cells.
let groupBy cmp (sq:seq<_>) =
let en = sq.GetEnumerator()
let next() = if en.MoveNext() then Some en.Current else None
(* this function returns a pair containing the first sequence and a lazy option indicating the first element in the next sequence (if any) *)
let rec seqStartingWith start =
match next() with
| Some y when cmp start y ->
let rest_next = lazy seqStartingWith y // delay evaluation until forced - stores the rest of this sequence and the start of the next one as a pair
seq { yield start; yield! fst (Lazy.force rest_next) },
lazy Lazy.force (snd (Lazy.force rest_next))
| next -> seq { yield start }, lazy next
let rec iter start =
seq {
match (Lazy.force start) with
| None -> ()
| Some start ->
let (first,next) = seqStartingWith start
yield first
yield! iter next
}
Seq.cache (iter (lazy next()))
Below is some code that does what I think you want. It is not idiomatic F#.
(It may be similar to Brian's answer, though I can't tell because I'm not familiar with the LazyList semantics.)
But it doesn't exactly match your test specification: Seq.length enumerates its entire input. Your "test code" calls Seq.length and then calls Seq.hd. That will generate an enumerator twice, and since there is no caching, things get messed up. I'm not sure if there is any clean way to allow multiple enumerators without caching. Frankly, seq<seq<'a>> may not be the best data structure for this problem.
Anyway, here's the code:
type State<'a> = Unstarted | InnerOkay of 'a | NeedNewInner of 'a | Finished
// f() = true means the neighbors should be kept together
// f() = false means they should be split
let split_up (f : 'a -> 'a -> bool) (input : seq<'a>) =
// simple unfold that assumes f captured a mutable variable
let iter f = Seq.unfold (fun _ ->
match f() with
| Some(x) -> Some(x,())
| None -> None) ()
seq {
let state = ref (Unstarted)
use ie = input.GetEnumerator()
let innerMoveNext() =
match !state with
| Unstarted ->
if ie.MoveNext()
then let cur = ie.Current
state := InnerOkay(cur); Some(cur)
else state := Finished; None
| InnerOkay(last) ->
if ie.MoveNext()
then let cur = ie.Current
if f last cur
then state := InnerOkay(cur); Some(cur)
else state := NeedNewInner(cur); None
else state := Finished; None
| NeedNewInner(last) -> state := InnerOkay(last); Some(last)
| Finished -> None
let outerMoveNext() =
match !state with
| Unstarted | NeedNewInner(_) -> Some(iter innerMoveNext)
| InnerOkay(_) -> failwith "Move to next inner seq when current is active: undefined behavior."
| Finished -> None
yield! iter outerMoveNext }
open System
let groupContigs (contigTime : TimeSpan) (holey : seq<DateTime * int>) =
split_up (fun (t1,_) (t2,_) -> (t2 - t1) <= contigTime) holey
// Test data
let numbers = {1 .. 15}
let contiguousTimeStamps =
let baseTime = DateTime.Now
seq { for n in numbers -> baseTime.AddMinutes(float n)}
let holeyData =
Seq.zip contiguousTimeStamps numbers
|> Seq.filter (fun (dateTime, num) -> num % 7 <> 0)
let grouped_data = groupContigs (new TimeSpan(0,1,0)) holeyData
printfn "Consuming..."
for group in grouped_data do
printfn "about to do a group"
for x in group do
printfn " %A" x
Ok, here's an answer I'm not unhappy with.
(EDIT: I am unhappy - it's wrong! No time to try to fix right now though.)
It uses a bit of imperative state, but it is not too difficult to follow (provided you recall that '!' is the F# dereference operator, and not 'not'). It is as lazy as possible, and takes a seq as input and returns a seq of seqs as output.
let N = 20
let data = // produce some arbitrary data with holes
seq {
for x in 1..N do
if x % 4 <> 0 && x % 7 <> 0 then
printfn "producing %d" x
yield x
}
let rec GroupBy comp (input:seq<_>) = seq {
let doneWithThisGroup = ref false
let areMore = ref true
use e = input.GetEnumerator()
let Next() = areMore := e.MoveNext(); !areMore
// deal with length 0 or 1, seed 'prev'
if not(e.MoveNext()) then () else
let prev = ref e.Current
while !areMore do
yield seq {
while not(!doneWithThisGroup) do
if Next() then
let next = e.Current
doneWithThisGroup := not(comp !prev next)
yield !prev
prev := next
else
// end of list, yield final value
yield !prev
doneWithThisGroup := true }
doneWithThisGroup := false }
let result = data |> GroupBy (fun x y -> y = x + 1)
printfn "Consuming..."
for group in result do
printfn "about to do a group"
for x in group do
printfn " %d" x