I am trying to write a program in sml that takes in the length of a list, the max number that will appear on the list and the list of course. It then calculates the length of the smallest "sub-list" that contains all numbers.
I have tried to use the sliding window approach , with two indexes , front and tail. The front scans first and when it finds a number it writes into a map how many times it has already seen this number. If the program finds all numbers then it calls the tail. The tail scans the list and if it finds that a number has been seen more times than 1 it takes it off.
The code I have tried so far is the following:
structure Key=
struct
type ord_key=int
val compare=Int.compare
end
fun min x y = if x>y then y else x;
structure mymap = BinaryMapFn ( Key );
fun smallest_sub(n,t,listall,map)=
let
val k=0
val front=0
val tail=0
val minimum= n;
val list1=listall;
val list2=listall;
fun increase(list1,front,k,ourmap)=
let
val number= hd list1
val elem=mymap.find(ourmap,number)
val per=getOpt(elem,0)+1
fun decrease(list2,tail,k,ourmap,minimum)=
let
val number=hd list2
val elem=mymap.find(ourmap,number)
val per=getOpt(elem,0)-1
val per1=getOpt(elem,0)
in
if k>t then
if (per1=1) then decrease(tl list2,tail+1,k-1,mymap.insert(ourmap,number,per),min minimum (front-tail))
else decrease(tl list2,tail+1,k,mymap.insert(ourmap,number,per),min minimum (front-tail))
else increase (list1, front,k,ourmap)
end
in
if t>k then
if (elem<>NONE) then increase (tl list1,front+1,k,mymap.insert(ourmap,number,per))
else increase(tl list1,front+1,k+1,mymap.insert(ourmap,number,per))
else (if (n>front) then decrease(list2,tail,k,ourmap,minimum) else minimum)
end
in
increase(list1,front,k,map)
end
fun solve (n,t,acc)= smallest_sub(n,t,acc,mymap.empty)
But when I call it with this smallest_sub(10,3,[1,3,1,3,1,3,3,2,2,1]); it does not work. What have I done wrong??
Example: if input is 1,3,1,3,1,3,3,2,2,1 the program should recognize that the parto of the list that contains all numbers and is the smallest is 1,3,3,2 and 3,2,2,1 so the output should be 4
This problem of "smallest sub-list that contains all values" seems to recur in
new questions without a successful answer. This is because it's not a minimal,
complete, and verifiable example.
Because you use a "sliding window" approach, indexing the front and the back
of your input, a list taking O(n) time to index elements is not ideal. You
really do want to use arrays here. If your input function must have a list, you
can convert it to an array for the purpose of the algorithm.
I'd like to perform a cleanup of the code before answering, because running
your current code by hand is a bit hard because it's so condensed. Here's an
example of how you could abstract out the book-keeping of whether a given
sub-list contains at least one copy of each element in the original list:
Edit: I changed the code below after originally posting it.
structure CountMap = struct
structure IntMap = BinaryMapFn(struct
type ord_key = int
val compare = Int.compare
end)
fun count (m, x) =
Option.getOpt (IntMap.find (m, x), 0)
fun increment (m, x) =
IntMap.insert (m, x, count (m, x) + 1)
fun decrement (m, x) =
let val c' = count (m, x)
in if c' <= 1
then NONE
else SOME (IntMap.insert (m, x, c' - 1))
end
fun flip f (x, y) = f (y, x)
val fromList = List.foldl (flip increment) IntMap.empty
end
That is, a CountMap is an int IntMap.map where the Int represents the
fixed key type of the map, being int, and the int parameter in front of it
represents the value type of the map, being a count of how many times this
value occurred.
When building the initialCountMap below, you use CountMap.increment, and
when you use the "sliding window" approach, you use CountMap.decrement to
produce a new countMap that you can test on recursively.
If you decrement the occurrence below 1, you're looking at a sub-list that
doesn't contain every element at least once; we rule out any solution by
letting CountMap.decrement return NONE.
With all of this machinery abstracted out, the algorithm itself becomes much
easier to express. First, I'd like to convert the list to an array so that
indexing becomes O(1), because we'll be doing a lot of indexing.
fun smallest_sublist_length [] = 0
| smallest_sublist_length (xs : int list) =
let val arr = Array.fromList xs
val initialCountMap = CountMap.fromList xs
fun go countMap i j =
let val xi = Array.sub (arr, i)
val xj = Array.sub (arr, j)
val decrementLeft = CountMap.decrement (countMap, xi)
val decrementRight = CountMap.decrement (countMap, xj)
in
case (decrementLeft, decrementRight) of
(SOME leftCountMap, SOME rightCountMap) =>
Int.min (
go leftCountMap (i+1) j,
go rightCountMap i (j-1)
)
| (SOME leftCountMap, NONE) => go leftCountMap (i+1) j
| (NONE, SOME rightCountMap) => go rightCountMap i (j-1)
| (NONE, NONE) => j - i + 1
end
in
go initialCountMap 0 (Array.length arr - 1)
end
This appears to work, but...
Doing Int.min (go left..., go right...) incurs a cost of O(n^2) stack
memory (in the case where you cannot rule out either being optimal). This is a
good use-case for dynamic programming because your recursive sub-problems have a
common sub-structure, i.e.
go initialCountMap 0 10
|- go leftCountMap 1 10
| |- ...
| `- go rightCountMap 1 9 <-.
`- go rightCountMap 0 9 | possibly same sub-problem!
|- go leftCountMap 1 9 <-'
`- ...
So maybe there's a way to store the recursive sub-problem in a memory array and not
perform a recursive lookup if you know the result to this sub-problem. How to
do memoization in SML is a good question in and of itself. How to do purely
functional memoization in a non-lazy language is an even better one.
Another optimization you could make is that if you ever find a sub-list the
size of the number of unique elements, you need to look no further. This number
is incidentally the number of elements in initialCountMap, and IntMap
probably has a function for finding it.
Related
In SML is it possible to find the number of occurrences of the min number in a list?
I have code to find the number of occurrences of a number but i am stumped on how to find the min and use it to find how many of the minimum num there is.
fun occurrences(nil, n)=0
| occurrences(ls, n) =
if hd(ls)=n then occurrences(tl(ls),n) + 1
else occurrences(tl(ls),n) + 0;
Thank you!
You can write a function that keeps track of the min value and its count as you iterate through the list.
We can do this by implementing a tail-recursive function which helper, which maintains the value of the current minimum and a count of the number of times that item has appeared.
We can then wrap this in another function min_count via a let-in-end block.
For example:
fun min_count [] = 0 (* the empty list has zero items in it *)
| min_count (x :: xs) =
let
(* when we reach the end of the list, return the accumulated count *)
fun helper (_, n) [] = n
| helper (m, n) (y :: ys) =
(* if we find a new minimum, reset the count *)
if y < m then helper (y, 1) ys
(* if the current list item is larger than the min, ignore it *)
else if y > m then helper (m, n) ys
(* if we've found another instance of the min, add one to the count *)
else helper (m, n + 1) ys
in
(* first item appears once *)
helper (x, 1) xs (* first item appears once *)
end;
This problem is a good test for using folds on a list.
Finding the mininum
If we want to find the minimum in a list we need to iterate over the list checking each element against a predetermined starting minimum value. If that element is less than that known minimum, we continue to iterate using that value instead. When we're done, we have the minimum value.
If the list is empty, there is no minimum value. If only one value is in the list, the minimum is obviously that. If there are more values, the starting minimum value is the first element.
We can use foldl to handle the iteration in this last case.
fun min([]) = NONE
| min([x]) = SOME x
| min(first::rest) =
SOME (foldl (fn (x, min) => if x < min then x else min)
first rest)
Finding occurrences
You've already done this, but this can be done in terms of a fold as well.
fun occurrences(lst, v) =
foldl (fn (x, count) => if x = v then count + 1 else count)
0 lst
Putting this together
We could use these two functions to find the number of times the minimum occurs in a list.
let
val numbers = [1, 4, 7, 2, 9, 0, 1, 6, 0]
val min = min(numbers)
val occ = case min of
NONE => NONE
| SOME x => SOME (occurrences(numbers, x))
in
case (min, occ) of
(NONE, NONE) => print("No minimum found.")
| (SOME m, SOME t) => print("Min: " ^ Int.toString(m) ^ "; times: " ^ Int.toString(t))
end
Can we do it in a single pass?
Using the above approach, we have to iterate over the list twice. This is a more general, but less efficient way of getting both pieces of information the minimum and the number of occurrences of it. We can use foldl to get both pieces of information, and it's going to look at lot like the definition of min.
We just need to pass a function to foldl that keeps a running tally of the number of times it has found the minimum value, and we need to pass it a tuple with both an initial minimum value and an initial count of 1.
fun minCount([]) = NONE
| minCount([x]) = SOME (x, 1)
| minCount(first::rest) =
SOME (foldl (fn (x, init as (min, count)) =>
case Int.compare(x, min) of
EQUAL => (min, count + 1)
| LESS => (x, 1)
| _ => init)
(first, 1)
rest)
With this function defined, our previous code can be rewritten as:
let
val numbers = [1, 4, 7, 2, 9, 0, 1, 6, 0]
val mc = minCount(numbers)
in
case mc of
NONE => print("No minimum found.")
| SOME (m, t) => print("Min: " ^ Int.toString(m) ^ "; times: " ^ Int.toString(t))
end
Assuming that you are supposed to use your occurrences function in the solution, write a function that finds the minimum,
fun minimum [x] = x
| minimum (x::xs) = let
val min = minimum xs
in
if x < min then x else min
end
Note that this does not handle the empty list.
You need to decide whether to leave the missing pattern as a runtime error, or add it and handle the error, for instance by raising an exception or by changing the return type to int option.
If you're taking a course, use one of the methods you've learned so far.
Then you can use that function,
occurrences(the_list, minimum the_list)
I'm new to SML and I'm attempting to get the index of an item in a list. I know that using List.nth will give me the value of an item at a index position, but I want the index value. There may even be a built in function that I'm not aware of. In my case, the list will not contain duplicates so if the item is in the list I get the index, if not it returns ~1. Here is the code I have so far. It works, but I don't think it is very clean:
val L=[1,2,3,4,5];
val m=length L-1;
fun Index(item, m, L)=if m<0 then ~1 else
if List.nth(L, m)=item then m else Index(item,m-1,L);
To elaborate on my previous comment, I suggest some changes for an implementation that fits better in the ML idiom:
fun index(item, xs) =
let
fun index'(m, nil) = NONE
| index'(m, x::xr) = if x = item then SOME m else index'(m + 1, xr)
in
index'(0, xs)
end
The individual changes are:
Have index return a value of type int option. NONE means the item is not in the list, SOME i means it is in the list, and the index of its first occurrence is i. This way, no special values (~1) need be used and the function's intended usage can be inferred from its type.
Hide the parameter m by renaming the function to index' and wrapping it into an outer function index that calls it with the appropriate arguments. The prime character (`) often indicates auxiliary values.
Use pattern matching on the list to get to the individual elements, eliminating the need for List.nth.
Also note that most commonly, function and variable names begin with a lowercase letter (index rather than Index), while capital letters are used for constructor constants (SOME) and the like.
I would like to propose a simpler and less efficient version of this index function. I agree that it is not as desirable to use exceptions rather than int option, and that it is not tail-recursive. But it is certainly easier to read and thus may serve as learning material:
fun index (x, []) = raise Subscript
| index (x, y::ys) =
if x = y then 0 else 1 + index (x, ys)
fun index(list,n)=
= if n=0 then hd(list) else index(tl(list),n-1);
val index = fn : 'a list * int -> 'a
index([1,2,3,4,5],2);
val it = 3 : int
index([1,2,3,4,5],0);
val it = 1 : int
I am having two lists in SML, lets say list A [(a,b,c),(d,e,f)] and list B [b,e]. I want to count how many occurrence of each item in B that matches the second element of each triple in A. The output should be 2. Because b and e each occurs once in A.
This is my code so far but my counter is always set to 0 when I move from one element to another in B. I know in Java this will just be a simple double for loop.
fun number_in_months (d : (int * int * int ) list, m : (int) list) =
if null m then 0
else if null d then number_in_months(d, tl m)
else if (#2(hd d)) = (hd m) then 1 + number_in_months (tl d, m)
else number_in_months(tl d, m)
The code is not accumulating a value between recursive calls. There may be other logic errors too.
Accumulating a value using recursion and functions is a common pattern which you can read more about here. It's essence is to deconstruct a list using head and tail until the list is empty and accumulate some value at each call. The sum function below is a simple example to show this. This could be adapted to your example to accumulate acc when b or e are found in list A.
fun sum(numbers: (int) list) =
let fun sumR(numbers: (int) list, acc: int) =
if null numbers
then acc
else
sumR(tl numbers, hd numbers + acc)
in
sumR(numbers, 0)
end
Running on [1,2,3] gives:
val sum = fn : int list -> int
- sum([1,2,3]);
val it = 6 : int
Note I am intentionally vague with this answer since this is a question regarding Coursera homework for the Programming Languages class.
As you mention, it would be a nested/double loop in any imperative programming language. What you are actually missing is the second loop.
Your "inner" loop goes through all elements of d, and when this is done, your "outer" loop tries to pop the top element of m and start all over, as seen from this line of your code:
else if null d then number_in_months(d, tl m)
However as you can see, you have just tested the list d to be empty and you supply this (exact same list) to your recursive call on the tail of m, which will then fall in this same case for each successive call until m is also empty and you return 0.
Thus what you are missing is to "keep a copy" of the original input list m. This can be done in various ways, but an inner (helper) function is properly the most used one and it even "looks" like a nested loop
fun number_in_months (d, m) =
let
fun nim' ([], y::ys) = nim (d, ys) (* 1 *)
| nim' (_, []) = 0 (* 2 *)
| nim' ((_, x2, _) :: xs, yss as (y::ys)) = ... (* 3 *)
in
nim'(d, m)
end
Using pattern matching the above code gets much simpler and less error prone. In case 1, the "inner" loop has gone through all elements in d, thus the recursive call using d from the outer function which is not changed at any time. In case 2, the "outer" loop has gone through all elements of m and we return 0 (the neutral element of addition). In case 3 we do the actual work. Here pattern matching is used such that we don't need to enforce the type of the argument and we don't need to pull out the 2nd element of the triple, we already have it in the variable x2. All that is needed is to do the computation and make a recursive call with xs and yss.
When doing it this way, the inner (helper) function is using a "copy" of the original input list d and stepping through its elements (potentially modifying it), but we always got a reference to the original input list, which we can use if/when needed.
Scenario:
If there is an array of integers and I want to get array of integers in return that their total should not exceed 10.
I am a beginner in Haskell and tried below. If any one could correct me, would be greatly appreciated.
numbers :: [Int]
numbers = [1,2,3,4,5,6,7,8,9,10, 11, 12]
getUpTo :: [Int] -> Int -> [Int]
getUpTo (x:xs) max =
if max <= 10
then
max = max + x
getUpTo xs max
else
x
Input
getUpTo numbers 0
Output Expected
[1,2,3,4]
BEWARE: This is not a solution to the knapsack problem :)
A very fast solution I came up with is the following one. Of course solving the full knapsack problem would be harder, but if you only need a quick solution this should work:
import Data.List (sort)
getUpTo :: Int -> [Int] -> [Int]
getUpTo max xs = go (sort xs) 0 []
where
go [] sum acc = acc
go (x:xs) sum acc
| x + sum <= max = go xs (x + sum) (x:acc)
| otherwise = acc
By sorting out the array before everything else, I can take items from the top one after another, until the maximum is exceeded; the list built up to that point is then returned.
edit: as a side note, I swapped the order of the first two arguments because this way should be more useful for partial applications.
For educational purposes (and since I felt like explaining something :-), here's a different version, which uses more standard functions. As written it is slower, because it computes a number of sums, and doesn't keep a running total. On the other hand, I think it expresses quite well how to break the problem down.
getUpTo :: [Int] -> [Int]
getUpTo = last . filter (\xs -> sum xs <= 10) . Data.List.inits
I've written the solution as a 'pipeline' of functions; if you apply getUpTo to a list of numbers, Data.List.inits gets applied to the list first, then filter (\xs -> sum xs <= 10) gets applied to the result, and finally last gets applied to the result of that.
So, let's see what each of those three functions do. First off, Data.List.inits returns the initial segments of a list, in increasing order of length. For example, Data.List.inits [2,3,4,5,6] returns [[],[2],[2,3],[2,3,4],[2,3,4,5],[2,3,4,5,6]]. As you can see, this is a list of lists of integers.
Next up, filter (\xs -> sum xs <= 10) goes through these lists of integer in order, keeping them if their sum is less than 10, and discarding them otherwise. The first argument of filter is a predicate which given a list xs returns True if the sum of xs is less than 10. This may be a bit confusing at first, so an example with a simpler predicate is in order, I think. filter even [1,2,3,4,5,6,7] returns [2,4,6] because that are the even values in the original list. In the earlier example, the lists [], [2], [2,3], and [2,3,4] all have a sum less than 10, but [2,3,4,5] and [2,3,4,5,6] don't, so the result of filter (\xs -> sum xs <= 10) . Data.List.inits applied to [2,3,4,5,6] is [[],[2],[2,3],[2,3,4]], again a list of lists of integers.
The last step is the easiest: we just return the last element of the list of lists of integers. This is in principle unsafe, because what should the last element of an empty list be? In our case, we are good to go, since inits always returns the empty list [] first, which has sum 0, which is less than ten - so there's always at least one element in the list of lists we're taking the last element of. We apply last to a list which contains the initial segments of the original list which sum to less than 10, ordered by length. In other words: we return the longest initial segment which sums to less than 10 - which is what you wanted!
If there are negative numbers in your numbers list, this way of doing things can return something you don't expect: getUpTo [10,4,-5,20] returns [10,4,-5] because that is the longest initial segment of [10,4,-5,20] which sums to under 10; even though [10,4] is above 10. If this is not the behaviour you want, and expect [10], then you must replace filter by takeWhile - that essentially stops the filtering as soon as the first element for which the predicate returns False is encountered. E.g. takeWhile [2,4,1,3,6,8,5,7] evaluates to [2,4]. So in our case, using takeWhile stops the moment the sum goes over 10, not trying longer segments.
By writing getUpTo as a composition of functions, it becomes easy to change parts of your algorithm: if you want the longest initial segment that sums exactly to 10, you can use last . filter (\xs -> sum xs == 10) . Data.List.inits. Or if you want to look at the tail segments instead, use head . filter (\xs -> sum xs <= 10) . Data.List.tails; or to take all the possible sublists into account (i.e. an inefficient knapsack solution!): last . filter (\xs -> sum xs <= 10) . Data.List.sortBy (\xs ys -> length xscomparelength ys) . Control.Monad.filterM (const [False,True]) - but I'm not going to explain that here, I've been rambling long enough!
There is an answer with a fast version; however, I thought it might also be instructive to see the minimal change necessary to your code to make it work the way you expect.
numbers :: [Int]
numbers = [1,2,3,4,5,6,7,8,9,10, 11, 12]
getUpTo :: [Int] -> Int -> [Int]
getUpTo (x:xs) max =
if max < 10 -- (<), not (<=)
then
-- return a list that still contains x;
-- can't reassign to max, but can send a
-- different value on to the next
-- iteration of getUpTo
x : getUpTo xs (max + x)
else
[] -- don't want to return any more values here
I am fairly new to Haskell. I just started with it a few hours ago and as such I see in every question a challenge that helps me get out of the imperative way of thinking and a opportunity to practice my recursion thinking :)
I gave some thought to the question and I came up with this, perhaps, naive solution:
upToBound :: (Integral a) => [a] -> a -> [a]
upToBound (x:xs) bound =
let
summation _ [] = []
summation n (m:ms)
| n + m <= bound = m:summation (n + m) ms
| otherwise = []
in
summation 0 (x:xs)
I know there is already a better answer, I just did it for the fun of it.
I have the impression that I changed the signature of the original invocation, because I thought it was pointless to provide an initial zero to the outer function invocation, since I can only assume it can only be zero at first. As such, in my implementation I hid the seed from the caller and provided, instead, the maximum bound, which is more likely to change.
upToBound [1,2,3,4,5,6,7,8,9,0] 10
Which outputs: [1,2,3,4]
I'm really new to F#, and I need a bit of help with an F# problem.
I need to implement a cut function that splits a list in half so that the output would be...
cut [1;2;3;4;5;6];;
val it : int list * int list = ([1; 2; 3], [4; 5; 6])
I can assume that the length of the list is even.
I'm also expected to define an auxiliary function gencut(n, xs) that cuts xs into two pieces, where n gives the size of the first piece:
gencut(2, [1;3;4;2;7;0;9]);;
val it : int list * int list = ([1; 3], [4; 2; 7; 0; 9])
I wouldn't normally ask for exercise help here, but I'm really at a loss as to where to even start. Any help, even if it's just a nudge in the right direction, would help.
Thanks!
Since your list has an even length, and you're cutting it cleanly in half, I recommend the following (psuedocode first):
Start with two pointers: slow and fast.
slow steps through the list one element at a time, fast steps two elements at a time.
slow adds each element to an accumulator variable, while fast moves foward.
When the fast pointer reaches the end of the list, the slow pointer will have only stepped half the number of elements, so its in the middle of the array.
Return the elements slow stepped over + the elements remaining. This should be two lists cut neatly in half.
The process above requires one traversal over the list and runs in O(n) time.
Since this is homework, I won't give a complete answer, but just to get you partway started, here's what it takes to cut the list cleanly in half:
let cut l =
let rec cut = function
| xs, ([] | [_]) -> xs
| [], _ -> []
| x::xs, y::y'::ys -> cut (xs, ys)
cut (l, l)
Note x::xs steps 1 element, y::y'::ys steps two.
This function returns the second half of the list. It is very easy to modify it so it returns the first half of the list as well.
You are looking for list slicing in F#. There was a great answer by #Juliet in this SO Thread: Slice like functionality from a List in F#
Basically it comes down to - this is not built in since there is no constant time index access in F# lists, but you can work around this as detailed. Her approach applied to your problem would yield a (not so efficient but working) solution:
let gencut(n, list) =
let firstList = list |> Seq.take n |> Seq.toList
let secondList = list |> Seq.skip n |> Seq.toList
(firstList, secondList)
(I didn't like my previous answer so I deleted it)
The first place to start when attacking list problems is to look at the List module which is filled with higher order functions which generalize many common problems and can give you succinct solutions. If you can't find anything suitable there, then you can look at the Seq module for solutions like #BrokenGlass demonstrated (but you can run into performance issues there). Next you'll want to consider recursion and pattern matching. There are two kinds of recursion you'll have to consider when processing lists: tail and non-tail. There are trade-offs. Tail-recursive solutions involve using an accumulator to pass state around, allowing you to place the recursive call in the tail position and avoid stack-overflows with large lists. But then you'll typically end up with a reversed list! For example,
Tail-recursive gencut solution:
let gencutTailRecursive n input =
let rec gencut cur acc = function
| hd::tl when cur < n ->
gencut (cur+1) (hd::acc) tl
| rest -> (List.rev acc), rest //need to reverse accumulator!
gencut 0 [] input
Non-tail-recursive gencut solution:
let gencutNonTailRecursive n input =
let rec gencut cur = function
| hd::tl when cur < n ->
let x, y = gencut (cur+1) tl //stackoverflow with big lists!
hd::x, y
| rest -> [], rest
gencut 0 input
Once you have your gencut solution, it's really easy to define cut:
let cut input = gencut ((List.length input)/2) input
Here's yet another way to do it using inbuilt library functions, which may or may not be easier to understand than some of the other answers. This solution also only requires one traversal across the input. My first thought after I looked at your problem was that you want something along the lines of List.partition, which splits a list into two lists based on a given predicate. However, in your case this predicate would be based on the index of the current element, which partition cannot handle, short of looking up the index for each element.
We can accomplish creating our own equivalent of this behavior using a fold or foldBack. I will use foldBack here as it means you won't have to reverse the lists afterward (see Stephens excellent answer). What we are going to do here is use the fold to provide our own index, along with the two output lists, all as the accumulator. Here is the generic function that will split your list into two lists based on n index:
let gencut n input =
//calculate the length of the list first so we can work out the index
let inputLength = input |> List.length
let results =
List.foldBack( fun elem acc->
let a,b,index = acc //decompose accumulator
if (inputLength - index) <= n then (elem::a,b,index+1)
else (a,elem::b,index+1) ) input ([],[],0)
let a,b,c = results
(a,b) //dump the index, leaving the two lists as output.
So here you see we start the foldBack with an initial accumulator value of ([],[],0). However, because we are starting at the end of the list, the 0 representing the current index needs to be subtracted from the total length of the list to get the actual index of the current element.
Then we simply check if the current index falls within the range of n. If it does, we update the accumulator by adding the current element to list a, leave list b alone, and increase the index by 1 : (elem::a,b,index+1). In all other cases, we do exactly the same but add the element to list b instead: (a,elem::b,index+1).
Now you can easily create your function that splits a list in half by creating another function over this one like so:
let cut input =
let half = (input |> List.length) / 2
input |> gencut half
I hope that can help you somewhat!
> cut data;;
val it : int list * int list = ([1; 2; 3], [4; 5; 6])
> gencut 5 data;;
val it : int list * int list = ([1; 2; 3; 4; 5], [6])
EDIT: you could avoid the index negation by supplying the length as the initial accumulator value and negating it on each cycle instead of increasing it - probably simpler that way :)
let gencut n input =
let results =
List.foldBack( fun elem acc->
let a,b,index = acc //decompose accumulator
if index <= n then (elem::a,b,index-1)
else (a,elem::b,index-1) ) input ([],[],List.length input)
let a,b,c = results
(a,b) //dump the index, leaving the two lists as output.
I have the same Homework, this was my solution. I'm just a student and new in F#
let rec gencut(n, listb) =
let rec cut n (lista : int list) (listb : int list) =
match (n , listb ) with
| 0, _ -> lista, listb
| _, [] -> lista, listb
| _, b :: listb -> cut (n - 1) (List.rev (b :: lista )) listb
cut n [] listb
let cut xs = gencut((List.length xs) / 2, xs)
Probably is not the best recursive solution, but it works. I think
You can use List.nth for random access and list comprehensions to generate a helper function:
let Sublist x y data = [ for z in x..(y - 1) -> List.nth data z ]
This will return items [x..y] from data. Using this you can easily generate gencut and cut functions (remember to check bounds on x and y) :)
check this one out:
let gencut s xs =
([for i in 0 .. s - 1 -> List.nth xs i], [for i in s .. (List.length xs) - 1 -> List.nth xs i])
the you just call
let cut xs =
gencut ((List.length xs) / 2) xs
with n durationn only one iteration split in two