I stumbled upon a strange time complexity behaviour when using Seq.unfold. Here's the minimal case I could come up with to reproduce this.
let idUnfolder sequence =
sequence
|> Seq.tryHead
|> Option.map (fun head -> (head, Seq.tail sequence))
let seqIdWithUnfold sequence =
Seq.unfold idUnfolder sequence
The function seqIdWithUnfold returns the given sequence itself. I would expect the resulting sequence to be iterated in linear time as Seq.unfold is O(n), Seq.tryHead and Seq.tail are O(1) (correct me if I'm wrong). However, against all my knowledge, it has a cubic complexity.
I tested the execution time with the following function with a set of n values.
let test n =
let start = System.DateTime.Now
Seq.init n id
|> seqIdWithUnfold
|> Seq.iter ignore
let duration = System.DateTime.Now - start
printfn "%f" duration.TotalSeconds
What makes this operation cubic in complexity?
seq is almost always O(n).
A seq aka IEnumerable<T> is essentially:
type Enumerator<'a> = {
getNext : unit -> 'a option
}
type Seq<'a> = {
getEnumerator: unit -> Enumerator<'a>
}
Every time you evaluate a sequence, a new Enumerator is created which captures the state of enumeration. getNext is then repeatedly called till the sequence terminates.
You can see this for yourself, if you replace a seq at any point with with
source |> Seq.map(fun x -> printfn "Eval %A" x; x)
Let's show the calls to getEnumerator as well:
let sq =
seq {
let mutable ctr = 0
do printfn "Init _"
while true do
ctr <- ctr + 1
printfn "Yield %d" ctr
yield ctr
}
seqIdWithUnfold (sq |> Seq.take 3) |> Seq.toList |> printfn "%A"
And there's the output:
Init _
Yield 1
Init _
Yield 1
Yield 2
Init _
Yield 1
Yield 2
Yield 3
Init _
Yield 1
Yield 2
Yield 3
[1; 2; 3]
This shows you the classic n(n+1)/2 pattern.
You can see that complexity will have n + n2 terms in it.
If can use list, you'll get O(n) instead of O(n^2).
If you really want O(1), use Arrays.
As Philip Carter mentioned in the comments Seq.tail is O(n).
I'm not sure why an F# List would be unacceptable, F# Lists are singly linked lists so you can get the constant time tail. If you need to accept any sequence as your signature just convert to a List before unfolding, it still makes it O(n), This example you presented can't be lazy anyway unless you really didn't need the tail.
let seqIdWithUnfold sequence =
sequence
|> Seq.toList
|> List.unfold idUnfolder
|> List.toSeq
Your processing example still works with the List module
let idUnfolder listSeq =
listSeq
|> List.tryHead
|> Option.map (fun head -> (head, List.tail listSeq))
But I think it would look a bit cleaner as
let idUnfolder =
function | [] -> None
| h::t -> Some(h, t);
Benchamarks
| Method | Mean | Error | StdDev |
|---------------- |------------:|----------:|----------:|
| Original | 4,683.88 us | 36.462 us | 34.106 us |
| ListConversion | 15.63 us | 0.202 us | 0.179 us |
// * Hints *
Outliers
Benchmarkem.ListConversion: Default -> 1 outlier was removed (16.53 us)
// * Legends *
Mean : Arithmetic mean of all measurements
Error : Half of 99.9% confidence interval
StdDev : Standard deviation of all measurements
1 us : 1 Microsecond (0.000001 sec)
Related
From an unordered list of int, I want to have the smallest difference between two elements. I have a code that is working but way to slow. Can anyone sugest some change to improve the performance? Please explain why you did the change and what will be the performance gain.
let allInt = [ 5; 8; 9 ]
let sortedList = allInt |> List.sort;
let differenceList = [ for a in 0 .. N-2 do yield sortedList.Item a - sortedList.Item a + 1 ]
printfn "%i" (List.min differenceList) // print 1 (because 9-8 smallest difference)
I think I'm doing to much list creation or iteration but I don't know how to write it differently in F#...yet.
Edit: I'm testing this code on list with 100 000 items or more.
Edit 2: I believe that if I can calculte the difference and have the min in one go it should improve the perf a lot, but I don't know how to do that, anay idea?
Thanks in advance
The List.Item performs in O(n) time and is probably the main performance bottle neck in your code. The evaluation of differenceList iterates the elements of sortedList by index, which means the performance is around O((N-2)(2(N-2))), which simplifies to O(N^2), where N is the number of elements in sortedList. For long lists, this will eventually perform badly.
What I would do is to eliminate calls to Item and instead use the List.pairwise operation
let data =
[ let rnd = System.Random()
for i in 1..100000 do yield rnd.Next() ]
#time
let result =
data
|> List.sort
|> List.pairwise // convert list from [a;b;c;...] to [(a,b); (b,c); ...]
|> List.map (fun (a,b) -> a - b |> abs) // Calculates the absolute difference
|> List.min
#time
The #time directives lets me measure execution time in F# Interactive and the output I get when running this code is:
--> Timing now on
Real: 00:00:00.029, CPU: 00:00:00.031, GC gen0: 1, gen1: 1, gen2: 0
val result : int = 0
--> Timing now off
F#'s built-in list type is implemented as a linked list, which means accessing elements by index has to enumerate the list all the way to the index each time. In your case you have two index accesses repeated N-2 times, getting slower and slower with each iteration, as the index grows and each access needs to go through longer part of the list.
First way out of this would be using an array instead of a list, which is a trivial change, but grants you faster index access.
(*
[| and |] let you define an array literal,
alternatively use List.toArray allInt
*)
let allInt = [| 5; 8; 9 |]
let sortedArray = allInt |> Array.sort;
let differenceList = [ for a in 0 .. N-2 do yield sortedArray.[a] - sortedArray.[a + 1] ]
Another approach might be pairing up the neighbours in the list, subtracting them and then finding a min.
let differenceList =
sortedList
|> List.pairwise
|> List.map (fun (x,y) -> x - y)
List.pairwise takes a list of elements and returns a list of the neighbouring pairs. E.g. in your example List.pairwise [ 5; 8; 9 ] = [ (5, 8); (8, 9) ], so that you can easily work with the pairs in the next step, the subtraction mapping.
This way is better, but these functions from List module take a list as input and produce a new list as the output, having to pass through the list 3 times (1 for pairwise, 1 for map, 1 for min at the end). To solve this, you can use functions from the Seq module, which work with .NETs IEnumerable<'a> interface allowing lazy evaluation resulting usually in fewer passes.
Fortunately in this case Seq defines alternatives for all the functions we use here, so the next step is trivial:
let differenceSeq =
sortedList
|> Seq.pairwise
|> Seq.map (fun (x,y) -> x - y)
let minDiff = Seq.min differenceSeq
This should need only one enumeration of the list (excluding the sorting phase of course).
But I cannot guarantee you which approach will be fastest. My bet would be on simply using an array instead of the list, but to find out, you will have to try it out and measure for yourself, on your data and your hardware. BehchmarkDotNet library can help you with that.
The rest of your question is adequately covered by the other answers, so I won't duplicate them. But nobody has yet addressed the question you asked in your Edit 2. To answer that question, if you're doing a calculation and then want the minimum result of that calculation, you want List.minBy. One clue that you want List.minBy is when you find yourself doing a map followed by a min operation (as both the other answers are doing): that's a classic sign that you want minBy, which does that in one operation instead of two.
There's one gotcha to watch out for when using List.minBy: It returns the original value, not the result of the calculation. I.e., if you do ints |> List.pairwise |> List.minBy (fun (a,b) -> abs (a - b)), then what List.minBy is going to return is a pair of items, not the difference. It's written that way because if it gives you the original value but you really wanted the result, you can always recalculate the result; but if it gave you the result and you really wanted the original value, you might not be able to get it. (Was that difference of 1 the difference between 8 and 9, or between 4 and 5?)
So in your case, you could do:
let allInt = [5; 8; 9]
let minPair =
allInt
|> List.pairwise
|> List.minBy (fun (x,y) -> abs (x - y))
let a, b = minPair
let minDifference = abs (a - b)
printfn "The difference between %d and %d was %d" a b minDifference
The List.minBy operation also exists on sequences, so if your list is large enough that you want to avoid creating an intermediate list of pairs, then use Seq.pairwise and Seq.minBy instead:
let allInt = [5; 8; 9]
let minPair =
allInt
|> Seq.pairwise
|> Seq.minBy (fun (x,y) -> abs (x - y))
let a, b = minPair
let minDifference = abs (a - b)
printfn "The difference between %d and %d was %d" a b minDifference
EDIT: Yes, I see that you've got a list of 100,000 items. So you definitely want the Seq version of this. The F# seq type is just IEnumerable, so if you're used to C#, think of the Seq functions as LINQ expressions and you'll have the right idea.
P.S. One thing to note here: see how I'm doing let a, b = minPair? That's called destructuring assignment, and it's really useful. I could also have done this:
let a, b =
allInt
|> Seq.pairwise
|> Seq.minBy (fun (x,y) -> abs (x - y))
and it would have given me the same result. Seq.minBy returns a tuple of two integers, and the let a, b = (tuple of two integers) expression takes that tuple, matches it against the pattern a, b, and thus assigns a to have the value of that tuple's first item, and b to have the value of that tuple's second item. Notice how I used the phrase "matches it against the pattern": this is the exact same thing as when you use a match expression. Explaining match expressions would make this answer too long, so I'll just point you to an excellent reference on them if you haven't already read it:
https://fsharpforfunandprofit.com/posts/match-expression/
Here is my solution:
let minPair xs =
let foo (x, y) = abs (x - y)
xs
|> List.allPairs xs
|> List.filter (fun (x, y) -> x <> y)
|> List.minBy foo
|> foo
I have this code in F# which finds the smallest positive number that is evenly divisible by all of the numbers from 1 to 20. It takes 10 seconds to complete.
let isDivisableByAll num (divisors: int[]) = Array.forall (fun div -> num % div = 0) divisors
let minNumDividedBy (divisors: int[]) =
let rec minNumDividedByAll stopAt acc =
if acc >= stopAt then 0
else if isDivisableByAll acc divisors then acc
else minNumDividedByAll stopAt (acc + 1)
minNumDividedByAll 400000000 1
minNumDividedBy [|1..20|]
So, I thought I could make it more elegant, because I prefer less code and wrote the following.
let answer = { 1..400000000 }
|> Seq.tryFind (fun el -> isDivisableByAll el [|1..20|])
It took 10 minutes! I couldn't explain the huge difference, since sequences are lazy. In an effort to investigate, I wrote an imperative loop.
let mutable i = 1
while i < 232792561 do
if isDivisableByAll i [|1..20|] then
printfn "%d" i
i <- i + 1
It took 8 minutes. Therefore, it's not the sequence's fault either, right? So, why is the initial function so fast? It can't be avoiding building up the stack, due to tail recursion, can it? Because I wouldn't expect a considerable stack if any, being built in the slow examples either.
It doesn't make much sense to me, can someone tell me?
Thank you.
If I understand correctly, you are trying to find how many numbers between 1 and 400000000 (inclusive) are divisible by all the numbers from 1 to 20. I made my own crude version of it:
let factors = Array.rev [| 2 .. 20 |]
let divisible f n =
Array.forall (fun x -> n % x = 0) f
let solution () =
{1 .. 400000000}
|> Seq.filter (divisible factors)
|> Seq.length
This solution takes over 90 seconds to run where I tested it. But I came to realize that it is a variation of Euler problem number 5, where we learn that 2520 is the first number divisible by all the numbers from 1 to 10. Using this fact, we can create a sequence of multiples of 2520, and test only the numbers from 11 to 19, as the multiples are guaranteed to be divisible by all the numbers from 1 to 10, and 20 as well:
let factors = Array.rev [| 11 .. 19 |]
let divisible f n =
Array.forall (fun x -> n % x = 0) f
let solution () =
Seq.initInfinite (fun i -> (i + 1) * 2520)
|> Seq.takeWhile (fun i -> i <= 400000000)
|> Seq.filter (divisible factors)
|> Seq.length
This solution takes 0.191 seconds.
If you don't know about Euler problem number 5, you can even algorithmically compute sequences with elements that are multiples of a given starting value. We feed the algorithm a sequence of numbers divisible by all numbers from 2 to n - 1, and it computes the first number divisible by all numbers from 2 to n. This is iterated through until we have a sequence of multiples of the first number divisible by all the factors we want:
let narrowDown m n s =
(s, {m .. n})
||> Seq.fold (fun a i ->
let j = Seq.find (fun x -> x % i = 0) a
Seq.initInfinite (fun i -> (i + 1) * j))
let solution () =
Seq.initInfinite (fun i -> i + 1)
|> narrowDown 2 20
|> Seq.takeWhile (fun i -> i <= 400000000)
|> Seq.length
This solution runs in 0.018 seconds.
As Fyodor Soikin commented, making a new array [|1..20|] for each iteration in the seq solution is the main culprit. If I define the array once and pass it in, I can run it in 10 seconds, compared to 27 seconds for the recursive solution. The remaining disparity must be down to the extra machinery needed around for a lazy sequence, compared to recursion that is tail-call optimised into a for loop.
Making the isDivisableByAll an inline function makes a significant difference for the recursive solution (down to 6 seconds). It doesn't seem to affect the seq solution.
I'm new to F# and I'm trying to figure out how to return a random string value from a list/array of strings.
I have a list like this:
["win8FF40", "win10Chrome45", "win7IE11"]
How can I randomly select and return one item from the list above?
Here is my first try:
let combos = ["win8FF40";"win10Chrome45";"win7IE11"]
let getrandomitem () =
let rnd = System.Random()
fun (combos : string[]) -> combos.[rnd.Next(combos.Length)]
Both the answers given here by latkin and mydogisbox are good, but I still want to add a third approach that I sometimes use. This approach isn't faster, but it's more flexible and more composable, and fast enough for small sequences. Depending on your needs, you can use one of higher performance options given here, or you can use the following.
Single-argument function using Random
Instead of directly enabling you to select a single element, I often define a shuffleR function like this:
open System
let shuffleR (r : Random) xs = xs |> Seq.sortBy (fun _ -> r.Next())
This function has the type System.Random -> seq<'a> -> seq<'a>, so it works with any sort of sequence: lists, arrays, collections, and lazily evaluated sequences (although not with infinite sequences).
If you want a single random element from a list, you can still do that:
> [1..100] |> shuffleR (Random ()) |> Seq.head;;
val it : int = 85
but you can also take, say, three randomly picked elements:
> [1..100] |> shuffleR (Random ()) |> Seq.take 3;;
val it : seq<int> = seq [95; 92; 12]
No-argument function
Sometimes, I don't care about having to pass in that Random value, so I instead define this alternative version:
let shuffleG xs = xs |> Seq.sortBy (fun _ -> Guid.NewGuid())
It works in the same way:
> [1..100] |> shuffleG |> Seq.head;;
val it : int = 11
> [1..100] |> shuffleG |> Seq.take 3;;
val it : seq<int> = seq [69; 61; 42]
Although the purpose of Guid.NewGuid() isn't to provide random numbers, it's often random enough for my purposes - random, in the sense of being unpredictable.
Generalised function
Neither shuffleR nor shuffleG are truly random. Due to the ways Random and Guid.NewGuid() work, both functions may result in slightly skewed distributions. If this is a concern, you can define an even more general-purpose shuffle function:
let shuffle next xs = xs |> Seq.sortBy (fun _ -> next())
This function has the type (unit -> 'a) -> seq<'b> -> seq<'b> when 'a : comparison. It can still be used with Random:
> let r = Random();;
val r : Random
> [1..100] |> shuffle (fun _ -> r.Next()) |> Seq.take 3;;
val it : seq<int> = seq [68; 99; 54]
> [1..100] |> shuffle (fun _ -> r.Next()) |> Seq.take 3;;
val it : seq<int> = seq [99; 63; 11]
but you can also use it with some of the cryptographically secure random number generators provided by the Base Class Library:
open System.Security.Cryptography
open System.Collections.Generic
let rng = new RNGCryptoServiceProvider ()
let bytes = Array.zeroCreate<byte> 100
rng.GetBytes bytes
let q = bytes |> Queue
FSI:
> [1..100] |> shuffle (fun _ -> q.Dequeue()) |> Seq.take 3;;
val it : seq<int> = seq [74; 82; 61]
Unfortunately, as you can see from this code, it's quite cumbersome and brittle. You have to know the length of the sequence up front; RNGCryptoServiceProvider implements IDisposable, so you should make sure to dispose of rng after use; and items will be removed from q after use, which means it's not reusable.
Cryptographically random sort or selection
Instead, if you really need a cryptographically correct sort or selection, it'd be easier to do it like this:
let shuffleCrypto xs =
let a = xs |> Seq.toArray
use rng = new RNGCryptoServiceProvider ()
let bytes = Array.zeroCreate a.Length
rng.GetBytes bytes
Array.zip bytes a |> Array.sortBy fst |> Array.map snd
Usage:
> [1..100] |> shuffleCrypto |> Array.head;;
val it : int = 37
> [1..100] |> shuffleCrypto |> Array.take 3;;
val it : int [] = [|35; 67; 36|]
This isn't something I've ever had to do, though, but I thought I'd include it here for the sake of completeness. While I haven't measured it, it's most likely not the fastest implementation, but it should be cryptographically random.
Your problem is that you are mixing Arrays and F# Lists (*type*[] is a type notation for Array). You could modify it like this to use lists:
let getrandomitem () =
let rnd = System.Random()
fun (combos : string list) -> List.nth combos (rnd.Next(combos.Length))
That being said, indexing into a List is usually a bad idea since it has O(n) performance since an F# list is basically a linked-list. You would be better off making combos into an array if possible like this:
let combos = [|"win8FF40";"win10Chrome45";"win7IE11"|]
I wrote a blog post on exactly this topic a while ago: http://latkin.org/blog/2013/11/16/selecting-a-random-element-from-a-linked-list-3-approaches-in-f/
3 approaches are given there, with discussion of performance and tradeoffs of each.
To summarize:
// pro: simple, fast in practice
// con: 2-pass (once to get length, once to select nth element)
let method1 lst (rng : Random) =
List.nth lst (rng.Next(List.length lst))
// pro: ~1 pass, list length is not bound by int32
// con: more complex, slower in practice
let method2 lst (rng : Random) =
let rec step remaining picks top =
match (remaining, picks) with
| ([], []) -> failwith "Don't pass empty list"
// if only 1 element is picked, this is the result
| ([], [p]) -> p
// if multiple elements are picked, select randomly from them
| ([], ps) -> step ps [] -1
| (h :: t, ps) ->
match rng.Next() with
// if RNG makes new top number, picks list is reset
| n when n > top -> step t [h] n
// if RNG ties top number, add current element to picks list
| n when n = top -> step t (h::ps) top
// otherwise ignore and move to next element
| _ -> step t ps top
step lst [] -1
// pro: exactly 1 pass
// con: more complex, slowest in practice due to tuple allocations
let method3 lst (rng : Random) =
snd <| List.fold (fun (i, pick) elem ->
if rng.Next(i) = 0 then (i + 1, elem)
else (i + 1, pick)
) (0, List.head lst) lst
Edit: I should clarify that above shows a few ways to get a random element from a list, assuming you must use a list. If it fits with the rest of your program's design, it is definitely more efficient to take a random element from an array.
I need a very efficient way to find duplicates in an unsorted sequence. This is what I came up with, but it has a few shortcomings, namely it
unnecessarily counts occurrences beyond 2
consumes the entire sequence before yielding duplicates
creates several intermediate sequences
module Seq =
let duplicates items =
items
|> Seq.countBy id
|> Seq.filter (snd >> ((<) 1))
|> Seq.map fst
Regardless of the shortcomings, I don't see a reason to replace this with twice the code. Is it possible to improve this with comparably concise code?
A more elegant functional solution:
let duplicates xs =
Seq.scan (fun xs x -> Set.add x xs) Set.empty xs
|> Seq.zip xs
|> Seq.choose (fun (x, xs) -> if Set.contains x xs then Some x else None)
Uses scan to accumulate sets of all elements seen so far. Then uses zip to combine each element with the set of elements before it. Finally, uses choose to filter out the elements that are in the set of previously-seen elements, i.e. the duplicates.
EDIT
Actually my original answer was completely wrong. Firstly, you don't want duplicates in your outputs. Secondly, you want performance.
Here is a purely functional solution that implements the algorithm you're after:
let duplicates xs =
(Map.empty, xs)
||> Seq.scan (fun xs x ->
match Map.tryFind x xs with
| None -> Map.add x false xs
| Some false -> Map.add x true xs
| Some true -> xs)
|> Seq.zip xs
|> Seq.choose (fun (x, xs) ->
match Map.tryFind x xs with
| Some false -> Some x
| None | Some true -> None)
This uses a map to track whether each element has been seen before once or many times and then emits the element if it is seen having only been seen once before, i.e. the first time it is duplicated.
Here is a faster imperative version:
let duplicates (xs: _ seq) =
seq { let d = System.Collections.Generic.Dictionary(HashIdentity.Structural)
let e = xs.GetEnumerator()
while e.MoveNext() do
let x = e.Current
let mutable seen = false
if d.TryGetValue(x, &seen) then
if not seen then
d.[x] <- true
yield x
else
d.[x] <- false }
This is around 2× faster than any of your other answers (at the time of writing).
Using a for x in xs do loop to enumerate the elements in a sequence is substantially slower than using GetEnumerator directly but generating your own Enumerator is not significantly faster than using a computation expression with yield.
Note that the TryGetValue member of Dictionary allows me to avoid allocation in the inner loop by mutating a stack allocated value whereas the TryGetValue extension member offered by F# (and used by kvb in his/her answer) allocates its return tuple.
Here's an imperative solution (which is admittedly slightly longer):
let duplicates items =
seq {
let d = System.Collections.Generic.Dictionary()
for i in items do
match d.TryGetValue(i) with
| false,_ -> d.[i] <- false // first observance
| true,false -> d.[i] <- true; yield i // second observance
| true,true -> () // already seen at least twice
}
This is the best "functional" solution I could come up with that doesn't consume the entire sequence up front.
let duplicates =
Seq.scan (fun (out, yielded:Set<_>, seen:Set<_>) item ->
if yielded.Contains item then
(None, yielded, seen)
else
if seen.Contains item then
(Some(item), yielded.Add item, seen.Remove item)
else
(None, yielded, seen.Add item)
) (None, Set.empty, Set.empty)
>> Seq.Choose (fun (x,_,_) -> x)
Assuming your sequence is finite, this solution requires one run on the sequence:
open System.Collections.Generic
let duplicates items =
let dict = Dictionary()
items |> Seq.fold (fun acc item ->
match dict.TryGetValue item with
| true, 2 -> acc
| true, 1 -> dict.[item] <- 2; item::acc
| _ -> dict.[item] <- 1; acc) []
|> List.rev
You can provide length of the sequence as the capacity of Dictionary, but it requires to enumerate the whole sequence once more.
EDIT:
To resolve 2nd problem, one could generate duplicates on demand:
open System.Collections.Generic
let duplicates items =
seq {
let dict = Dictionary()
for item in items do
match dict.TryGetValue item with
| true, 2 -> ()
| true, 1 -> dict.[item] <- 2; yield item
| _ -> dict.[item] <- 1
}
Functional solution:
let duplicates items =
let test (unique, result) v =
if not(unique |> Set.contains v) then (unique |> Set.add v ,result)
elif not(result |> Set.contains v) then (unique,result |> Set.add v)
else (unique, result)
items |> Seq.fold test (Set.empty, Set.empty) |> snd |> Set.toSeq
Background:
I have a sequence of contiguous, time-stamped data.
The data-sequence has holes in it, some large, others just a single missing value.
Whenever the hole is just a single missing value, I want to patch the holes using a dummy-value (larger holes will be ignored).
I would like to use lazy generation of the patched sequence, and I am thus using Seq.unfold.
I have made two versions of the method to patch the holes in the data.
The first consumes the sequence of data with holes in it and produces the patched sequence. This is what i want, but the methods runs horribly slow when the number of elements in the input sequence rises above 1000, and it gets progressively worse the more elements the input sequence contains.
The second method consumes a list of the data with holes and produces the patched sequence and it runs fast. This is however not what I want, since this forces the instantiation of the entire input-list in memory.
I would like to use the (sequence -> sequence) method rather than the (list -> sequence) method, to avoid having the entire input-list in memory at the same time.
Questions:
1) Why is the first method so slow (getting progressively worse with larger input lists)
(I am suspecting that it has to do with repeatedly creating new sequences with Seq.skip 1, but I am not sure)
2) How can I make the patching of holes in the data fast, while using an input sequence rather than an input list?
The code:
open System
// Method 1 (Slow)
let insertDummyValuesWhereASingleValueIsMissing1 (timeBetweenContiguousValues : TimeSpan) (values : seq<(DateTime * float)>) =
let sizeOfHolesToPatch = timeBetweenContiguousValues.Add timeBetweenContiguousValues // Only insert dummy-values when the gap is twice the normal
(None, values) |> Seq.unfold (fun (prevValue, restOfValues) ->
if restOfValues |> Seq.isEmpty then
None // Reached the end of the input seq
else
let currentValue = Seq.hd restOfValues
if prevValue.IsNone then
Some(currentValue, (Some(currentValue), Seq.skip 1 restOfValues )) // Only happens to the first item in the seq
else
let currentTime = fst currentValue
let prevTime = fst prevValue.Value
let timeDiffBetweenPrevAndCurrentValue = currentTime.Subtract(prevTime)
if timeDiffBetweenPrevAndCurrentValue = sizeOfHolesToPatch then
let dummyValue = (prevTime.Add timeBetweenContiguousValues, 42.0) // 42 is chosen here for obvious reasons, making this comment superfluous
Some(dummyValue, (Some(dummyValue), restOfValues))
else
Some(currentValue, (Some(currentValue), Seq.skip 1 restOfValues))) // Either the two values were contiguous, or the gap between them was too large to patch
// Method 2 (Fast)
let insertDummyValuesWhereASingleValueIsMissing2 (timeBetweenContiguousValues : TimeSpan) (values : (DateTime * float) list) =
let sizeOfHolesToPatch = timeBetweenContiguousValues.Add timeBetweenContiguousValues // Only insert dummy-values when the gap is twice the normal
(None, values) |> Seq.unfold (fun (prevValue, restOfValues) ->
match restOfValues with
| [] -> None // Reached the end of the input list
| currentValue::restOfValues ->
if prevValue.IsNone then
Some(currentValue, (Some(currentValue), restOfValues )) // Only happens to the first item in the list
else
let currentTime = fst currentValue
let prevTime = fst prevValue.Value
let timeDiffBetweenPrevAndCurrentValue = currentTime.Subtract(prevTime)
if timeDiffBetweenPrevAndCurrentValue = sizeOfHolesToPatch then
let dummyValue = (prevTime.Add timeBetweenContiguousValues, 42.0)
Some(dummyValue, (Some(dummyValue), currentValue::restOfValues))
else
Some(currentValue, (Some(currentValue), restOfValues))) // Either the two values were contiguous, or the gap between them was too large to patch
// Test data
let numbers = {1.0..10000.0}
let contiguousTimeStamps = seq { for n in numbers -> DateTime.Now.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))
// The fast sequence-patching (method 2)
dataWithOccationalHoles |> List.of_seq |> insertDummyValuesWhereASingleValueIsMissing2 timeBetweenContiguousValues |> Seq.iter (fun pair -> printfn "%f %s" (snd pair) ((fst pair).ToString()))
// The SLOOOOOOW sequence-patching (method 1)
dataWithOccationalHoles |> insertDummyValuesWhereASingleValueIsMissing1 timeBetweenContiguousValues |> Seq.iter (fun pair -> printfn "%f %s" (snd pair) ((fst pair).ToString()))
Any time you break apart a seq using Seq.hd and Seq.skip 1 you are almost surely falling into the trap of going O(N^2). IEnumerable<T> is an awful type for recursive algorithms (including e.g. Seq.unfold), since these algorithms almost always have the structure of 'first element' and 'remainder of elements', and there is no efficient way to create a new IEnumerable that represents the 'remainder of elements'. (IEnumerator<T> is workable, but its API programming model is not so fun/easy to work with.)
If you need the original data to 'stay lazy', then you should use a LazyList (in the F# PowerPack). If you don't need the laziness, then you should use a concrete data type like 'list', which you can 'tail' into in O(1).
(You should also check out Avoiding stack overflow (with F# infinite sequences of sequences) as an FYI, though it's only tangentially applicable to this problem.)
Seq.skip constructs a new sequence. I think that is why your original approach is slow.
My first inclination is to use a sequence expression and Seq.pairwise. This is fast and easy to read.
let insertDummyValuesWhereASingleValueIsMissingSeq (timeBetweenContiguousValues : TimeSpan) (values : seq<(DateTime * float)>) =
let sizeOfHolesToPatch = timeBetweenContiguousValues.Add timeBetweenContiguousValues // Only insert dummy-values when the gap is twice the normal
seq {
yield Seq.hd values
for ((prevTime, _), ((currentTime, _) as next)) in Seq.pairwise values do
let timeDiffBetweenPrevAndCurrentValue = currentTime.Subtract(prevTime)
if timeDiffBetweenPrevAndCurrentValue = sizeOfHolesToPatch then
let dummyValue = (prevTime.Add timeBetweenContiguousValues, 42.0) // 42 is chosen here for obvious reasons, making this comment superfluous
yield dummyValue
yield next
}