I can use a list like a stack, but what is a proper way to create a queue in ATS? For instance, say I have the following pseudo code:
val xs = queue_create()
val () = xs.enqueue(1)
val () = xs.enqueue(2)
val () = print(xs.dequeue()) // print 1
val () = xs.enqueue(3)
val () = print(xs.dequeue()) // print 2
val () = print(xs.dequeue()) // print 3
I should see that 1, 2, and 3 are printed out.
Since I'm unaware of a queue type built in the ATS standard library, I offer my own basic implementation from one of my projects, adapted from the code referenced in the Linear Channels for Asynchronous IPC chapter of the Introduction to Programming in ATS book.
See a working example in this snippet.
You could go the long-winded way: by using two pointers to head and tail of a singly-linked list.
However, ensuring that this implementation actually works is not so trivial. It relies on data views to encode certain invariants, and so is quite complex, but the outwards-facing API is not hard to use, at all. I've also used "intrusive" linked lists, to make things simpler for the use-case.
The core definitions are as follows:
View for encoding singly-linked list segments: a segment is just a singly-linked where the "last" next pointer can point anywhere, including to NULL:
dataview slseg_v
(addr(*self*), addr(*last node's [next]*), int(*segment length*)) =
| {n:nat} {l1,l2,l3:addr}
slseg_v_cons (l1, l3, n+1) of (
mfree_gc_v (l1), slseg_node_v (l2, l1), slseg_v (l2, l3, n)
) // end of [slseg_v_cons]
| {l:addr} slseg_v_nil (l, l, 0)
// end of [slseg_v]
Type for a list-based queue: it is either empty, or contains a size, a pointer to head of segment, and a pointer to tail of segment:
datavtype queuelst_vt (int) =
| {n:nat} {l1,l2,l3:addr}
queuelst_vt_some (n+1) of (
slseg_v (l1, l2, n),
mfree_gc_v (l2),
slseg_node_v (null, l2)
| int n, ptr l1, ptr l2
) // end of [queuelst_vt_some]
| queuelst_vt_none (0) of ()
You can run the full code at Glot.io
Here's a bit cleaned up version of the same code -- this time, I tried to ensure that the code has clean interface/implementation separation.
Related
I am working on homework and the problem is where we get 2 int lists of the same size, and then add the numbers together. Example as follows.
vecadd [1;2;3] [4;5;6];; would return [5;7;9]
I am new to this and I need to keep my code pretty simple so I can learn from it. I have this so far. (Not working)
let rec vecadd L K =
if L <> [] then vecadd ((L.Head+K.Head)::L) K else [];;
I essentially want to just replace the first list (L) with the added numbers. Also I have tried to code it a different way using the match cases.
let rec vecadd L K =
match L with
|[]->[]
|h::[]-> L
|h::t -> vecadd ((h+K.Head)::[]) K
Neither of them are working and I would appreciate any help I can get.
First, your idea about modifying the first list instead of returning a new one is misguided. Mutation (i.e. modifying data in place) is the number one reason for bugs today (used to be goto, but that's been banned for a long time now). Making every operation produce a new datum rather than modify existing ones is much, much safer. And in some cases it may be even more performant, quite counterintuitively (see below).
Second, the way you're trying to do it, you're not doing what you think you're doing. The double-colon doesn't mean "modify the first item". It means "attach an item in front". For example:
let a = [1; 2; 3]
let b = 4 :: a // b = [4; 1; 2; 3]
let c = 5 :: b // c = [5; 4; 1; 2; 3]
That's how lists are actually built: you start with a empty list and prepend items to it. The [1; 2; 3] syntax you're using is just a syntactic sugar for that. That is, [1; 2; 3] === 1::2::3::[].
So how do I modify a list, you ask? The answer is, you don't! F# lists are immutable data structures. Once you've created a list, you can't modify it.
This immutability allows for an interesting optimization. Take another look at the example I posted above, the one with three lists a, b, and c. How many cells of memory do you think these three lists occupy? The first list has 3 items, second - 4, and third - 5, so the total amount of memory taken must be 12, right? Wrong! The total amount of memory taken up by these three lists is actually just 5 cells. This is because list b is not a block of memory of length 4, but rather just the number 4 paired with a pointer to the list a. The number 4 is called "head" of the list, and the pointer is called its "tail". Similarly, the list c consists of one number 5 (its "head") and a pointer to list b, which is its "tail".
If lists were not immutable, one couldn't organize them like this: what if somebody modifies my tail? Lists would have to be copied every time (google "defensive copy").
So the only way to do with lists is to return a new one. What you're trying to do can be described like this: if the input lists are empty, the result is an empty list; otherwise, the result is the sum of tails prepended with the sum of heads. You can write this down in F# almost verbatim:
let rec add a b =
match a, b with
| [], [] -> [] // sum of two empty lists is an empty list
| a::atail, b::btail -> (a + b) :: (add atail btail) // sum of non-empty lists is sum of their tails prepended with sum of their heads
Note that this program is incomplete: it doesn't specify what the result should be when one input is empty and the other is not. The compiler will generate a warning about this. I'll leave the solution as an exercise for the reader.
You can map over both lists together with List.map2 (see the docs)
It goes over both lists pairwise and you can give it a function (the first parameter of List.map2) to apply to every pair of elements from the lists. And that generates the new list.
let a = [1;2;3]
let b = [4;5;6]
let vecadd = List.map2 (+)
let result = vecadd a b
printfn "%A" result
And if you want't to do more work 'yourself' something like this?
let a = [1;2;3]
let b = [4;5;6]
let vecadd l1 l2 =
let rec step l1 l2 acc =
match l1, l2 with
| [], [] -> acc
| [], _ | _, [] -> failwithf "one list is bigger than the other"
| h1 :: t1, h2 :: t2 -> step t1 t2 (List.append acc [(h1 + h2)])
step l1 l2 []
let result = vecadd a b
printfn "%A" result
The step function is a recursive function that takes two lists and an accumulator to carry the result.
In the last match statement it does three things
Sum the head of both lists
Add the result to the accumulator
Recursively call itself with the new accumulator and the tails of the lists
The first match returns the accumulator when the remaining lists are empty
The second match returns an error when one of the lists is longer than the other.
The accumulator is returned as the result when the remaining lists are empty.
The call step l1 l2 [] kicks it off with the two supplied lists and an empty accumulator.
I have done this for crossing two lists (multiply items with same index together):
let items = [1I..50_000I]
let another = [1I..50_000I]
let rec cross a b =
let rec cross_internal = function
| r, [], [] -> r
| r, [], t -> r#t
| r, t, [] -> r#t
| r, head::t1, head2::t2 -> cross_internal(r#[head*head2], t1, t2)
cross_internal([], a, b)
let result = cross items another
result |> printf "%A,"
Note: not really performant. There are list object creations at each step which is horrible. Ideally the inner function cross_internal must create a mutable list and keep updating it.
Note2: my ranges were larger initially and using bigint (hence the I suffix in 50_000) but then reduced the sample code above to just 50,500 elements.
I have some difficulties to transfer imperative algorithms into a functional style. The main concept that I cannot wrap my head around is how to fill sequences with values according to their position in the sequence. How would an idiomatic solution for the following algorithm look in Haskell?
A = unsigned char[256]
idx <- 1
for(i = 0 to 255)
if (some_condition(i))
A[i] <- idx
idx++
else
A[i] = 0;
The algorithm basically creates a lookup table for the mapping function of a histogram.
Do you know any resources which would help me to understand this kind of problem better?
One of the core ideas in functional programming is to express algorithms as data transformations. In a lazy language like Haskell, we can even go a step further and think of lazy data structures as reified computations. In a very real sense, Haskell's lists are more like loops than normal linked lists: they can be calculated incrementally and don't have to exist in memory all at once. At the same time, we still get many of the advantages of having a data type like that ability to pass it around and inspect it with pattern matching.
With this in mind, the "trick" for expressing a for-loop with an index is to create a list of all the values it can take. Your example is probably the simplest case: i takes all the values from 0 to 255, so we can use Haskell's built-in notation for ranges:
[0..255]
At a high level, this is Haskell's equivalent of for (i = 0 to 255); we can then execute the actual logic in the loop by traversing this list either by a recursive function or a higher-order function from the standard library. (The second option is highly preferred.)
This particular logic is a good fit for a fold. A fold lets us take in a list item by item and build up a result of some sort. At each step, we get a list item and the value of our built-up result so far. In this particular case, we want to process the list from left to right while incrementing an index, so we can use foldl; the one tricky part is that it will produce the list backwards.
Here's the type of foldl:
foldl :: (b -> a -> b) -> b -> [a] -> b
So our function takes in our intermediate value and a list element and produces an updated intermediate value. Since we're constructing a list and keeping track of an index, our intermediate value will be a pair that contains both. Then, once we have the final result, we can ignore the idx value and reverse the final list we get:
a = let (result, _) = foldl step ([], 1) [0..255] in reverse result
where step (a, idx) i
| someCondition i = (idx:a, idx + 1)
| otherwise = (0:a, idx)
In fact, the pattern of transforming a list while keeping track of some intermediate state (idx in this case) is common enough so that it has a function of its own in terms of the State type. The core abstraction is a bit more involved (read through ["You Could Have Invented Monads"][you] for a great introduction), but the resulting code is actually quite pleasant to read (except for the imports, I guess :P):
import Control.Applicative
import Control.Monad
import Control.Monad.State
a = evalState (mapM step [0..255]) 1
where step i
| someCondition i = get <* modify (+ 1)
| otherwise = return 0
The idea is that we map over [0..255] while keeping track of some state (the value of idx) in the background. evalState is how we put all the plumbing together and just get our final result. The step function is applied to each input list element and can also access or modify the state.
The first case of the step function is interesting. The <* operator tells it to do the thing on the left first, the thing on the right second but return the value on the left. This lets us get the current state, increment it but still return the value we got before it was incremented. The fact that our notion of state is a first-class entity and we can have library functions like <* is very powerful—I've found this particular idiom really useful for traversing trees, and other similar idioms have been quite useful for other code.
There are several ways to approach this problem depending on what data structure you want to use. The simplest one would probably be with lists and the basic functions available in Prelude:
a = go 1 [] [0..255]
where
go idx out [] = out
go idx out (i:is) =
if condition i
then go (idx + 1) (out ++ [idx]) is
else go idx (out ++ [0]) is
This uses the worker pattern with two accumulators, idx and out, and it traverses down the last parameter until no more elements are left, then returns out. This could certainly be converted into a fold of some sort, but in any case it won't be very efficient, appending items to a list with ++ is very inefficient. You could make it better by using idx : out and 0 : out, then using reverse on the output of go, but it still isn't an ideal solution.
Another solution might be to use the State monad:
a = flip runState 1 $ forM [0..255] $ \i -> do
idx <- get
if condition i
then do
put $ idx + 1 -- idx++
return idx -- A[i] = idx
else return 0
Which certainly looks a lot more imperative. The 1 in flip runState 1 is indicating that your initial state is idx = 1, then you use forM (which looks like a for loop but really isn't) over [0..255], the loop variable is i, and then it's just a matter of implementing the rest of the logic.
If you want to go a lot more advanced you could use the StateT and ST monads to have an actual mutable array with a state at the same time. The explanation of how this works is far beyond the scope of this answer, though:
import Control.Monad.State
import Control.Monad.ST
import qualified Data.Vector as V
import qualified Data.Vector.Mutable as MV
a :: V.Vector Int
a = runST $ (V.freeze =<<) $ flip evalStateT (1 :: Int) $ do
a' <- lift $ MV.new 256
lift $ MV.set a' 0
forM_ [0..255] $ \i -> do
when (condition i) $ do
idx <- get
lift $ MV.write a' i idx
put $ idx + 1
return a'
I simplified it a bit so that each element is set to 0 from the start, we begin with an initial state of idx = 1, loop over [0..255], if the current index i meets the condition then get the current idx, write it to the current index, then increment idx. Run this as a stateful operation, then freeze the vector, and finally run the ST monad side of things. This allows for an actual mutable vector hidden safely within the ST monad so that the outside world doesn't know that to calculate a you have to do some rather strange things.
Explicit recursion:
a = go 0 1
where go 256 _ = []
go i idx | someCondition i = idx : go (i+1) (idx+1)
| otherwise = 0 : go (i+1) idx
Unfolding: (variant of the explicit recursion above)
a = unfoldr f (0,1)
where f (256,_) = Nothing
f (i,idx) | someCondition i = Just (idx,(i+1,idx+1))
| otherwise = Just (0 ,(i+1,idx ))
Loops can usually be expressed using different fold functions. Here is a solution which uses foldl(you can switch to foldl' if you run into a stackoverflow error):
f :: (Num a) => (b -> Bool) -> a -> [b] -> [a]
f pred startVal = reverse . fst . foldl step ([], startVal)
where
step (xs, curVal) x
| pred x = (curVal:xs, curVal + 1)
| otherwise = (0:xs, curVal)
How to use it? This function takes a predicate (someCondition in your code), the initial value of an index and a list of element to iterate over. That is, you can call f someCondition 1 [0..255] to obtain the result for the example from your question.
So I have a couple of questions, as a newbie trying to learn O'Caml.
In functions, I often times see a | what does that mean? Also, why are functions some times defined as:
let rec a = function
Why does it specifically equal to function and then the code?
My main question however is, I was trying to write a function that would count the number of times an element exists in a list, so if I had 1, 5,5,6,9 with the target val as 5, then I'd return 2, if target val was 9, then I'd return 1, since it repeats once.
here is my attempt, please tell me what I'm doing wrong:
let rec track (x, l)= let rec helper(x,l, count)
in counthelper
match l with [] --> count
| (a::as) -> if(x = a)
then helper(as,l, count+1)
else count( as, l, count);;
The match and function keywords take a list of patterns to be matched. The | symbol is used to separate the different patterns. That's why it shows up so frequently in OCaml code.
The function keyword is like an abbreviation for fun and match. It lets you define a function as a set of patterns to be matched against an argument.
Your code has let rec helper (x, l, count) in .... This isn't a proper let expression. You want something like this: let helper (x, l, count) = def in expr.
More generally your code might look like this:
let track (x, l) =
let rec helper (x, l, count) =
... definition of helper ...
in
helper (x, l, 0)
As a side comment, you're using tuples for function parameters. It's more idiomatic in OCaml to use currying, i.e., to have separate parameters more like this:
let track x l =
...
This lets you do partial application (specify only some of the parameters), and also is cleaner syntactically.
Update
Your latest code doesn't return a value because it has infinite recursion.
Usually | means pattern matching.
let rec means that function can be recursive (call itself). Tutorial.
This is my solution where some useful symbols are changed to _ symbols. Let it be an exercise for you:
let rec count y xs =
let rec inner n = function
| __ -> n
| ______________ -> inner (n+1) xs
| ____ -> inner n xs
in
inner 0 xs;;
Your implementation has some issues.
The most obvious one is that you are using as in pattern matching. You can't us keyword in pattern matching this way.
You need to reread chapter about function declarations. It seems that you are mixing it with function invocation.
You are using not curried functions. You did some in C before, don't you?
You are using if when using using when is nicer. This construction is called guard.
I am solving the Programming assinment for Harvard CS 51 programming course in ocaml.
The problem is to define a function that can compress a list of chars to list of pairs where each pair contains a number of consequent occurencies of the character in the list and the character itself, i.e. after applying this function to the list ['a';'a';'a';'a';'a';'b';'b';'b';'c';'d';'d';'d';'d'] we should get the list of [(5,'a');(3,'b');(1,'c');(4,'d')].
I came up with the function that uses auxiliary function go to solve this problem:
let to_run_length (lst : char list) : (int*char) list =
let rec go i s lst1 =
match lst1 with
| [] -> [(i,s)]
| (x::xs) when s <> x -> (i,s) :: go 0 x lst1
| (x::xs) -> go (i + 1) s xs
in match lst with
| x :: xs -> go 0 x lst
| [] -> []
My question is: Is it possible to define recursive function to_run_length with nested pattern matching without defining an auxiliary function go. How in this case we can store a state of counter of already passed elements?
The way you have implemented to_run_length is correct, readable and efficient. It is a good solution. (only nitpick: the indentation after in is wrong)
If you want to avoid the intermediary function, you must use the information present in the return from the recursive call instead. This can be described in a slightly more abstract way:
the run length encoding of the empty list is the empty list
the run length encoding of the list x::xs is,
if the run length encoding of xs start with x, then ...
if it doesn't, then (x,1) ::run length encoding of xs
(I intentionally do not provide source code to let you work the detail out, but unfortunately there is not much to hide with such relatively simple functions.)
Food for thought: You usually encounter this kind of techniques when considering tail-recursive and non-tail-recursive functions (what I've done resembles turning a tail-rec function in non-tail-rec form). In this particular case, your original function was not tail recursive. A function is tail-recursive when the flows of arguments/results only goes "down" the recursive calls (you return them, rather than reusing them to build a larger result). In my function, the flow of arguments/results only goes "up" the recursive calls (the calls have the least information possible, and all the code logic is done by inspecting the results). In your implementation, flows goes both "down" (the integer counter) and "up" (the encoded result).
Edit: upon request of the original poster, here is my solution:
let rec run_length = function
| [] -> []
| x::xs ->
match run_length xs with
| (n,y)::ys when x = y -> (n+1,x)::ys
| res -> (1,x)::res
I don't think it is a good idea to write this function. Current solution is OK.
But if you still want to do it you can use one of two approaches.
1) Without changing arguments of your function. You can define some toplevel mutable values which will contain accumulators which are used in your auxilary function now.
2) You can add argument to your function to store some data. You can find some examples when googling for continuation-passing style.
Happy hacking!
P.S. I still want to underline that your current solution is OK and you don't need to improve it!
In the following generalized code:
nat = [1..xmax]
xmax = *insert arbitrary Integral value here*
setA = [2*x | x <- nat]
setB = [3*x | x <- nat]
setC = [4*x | x <- nat]
setD = [5*x | x <- nat]
setOne = setA `f` setB
setTwo = setC `f` setD
setAll = setOne ++ setTwo
setAllSorted = quicksort setAll
(please note that 'f' stands for a function of type
f :: Integral a => [a] -> [a] -> [a]
that is not simply ++)
how does Haskell handle attempting to print setAllSorted?
does it get the values for setA and setB, compute setOne and then only keep the values for setOne in memory (before computing everything else)?
Or does Haskell keep everything in memory until having gotten the value for setAllSorted?
If the latter is the case then how would I specify (using main, do functions and all that other IO stuff) that it do the former instead?
Can I tell the program in which order to compute and garbage collect? If so, how would I do that?
The head of setAllSorted is necessarily less-than-or-equal to every element in the tail. Therefore, in order to determine the head, all of setOne and setTwo must be computed. Furthermore, since all of the sets are constant applicative forms, I believe they will not be garbage collected after being computed. The numbers themselves will likely be shared between the sets, but the cons nodes that glue them together will likely not be (your luck with some will depend upon the definition of f).
Due to laziness, Haskell evaluates things on-demand. You can think of the printing done at the end as "pulling" elements from the list setAllSorted, and that might pull other things with it.
That is, running this code it goes something like this:
Printing first evaluates the first element of setAllSorted.
Since this comes from a sorting procedure, it will require all the elements of setAll to be evaluated. (Since the smallest element could be the last one).
Evaluating the first element of setAll requires evaluating the first element of setOne.
Evaluating the first element of setOne depends on how f is implemented. It might require all or none of setA and setB to be evaluated.
After we're done printing the first element of setAllSorted, setAll will have been fully evaluated. There are no more references to setOne, setTwo and the smaller sets, so all of these are by now eligible for garbage collection. The first element of setAllSorted can also be reclaimed.
So in theory, this code will keep setAll in memory most of the time, while setAllSorted, setOne and setTwo will likely only occupy a constant amount of space at any time. Depending on the implementation of f, the same may be true for the smaller sets.