Batteries.LazyList allows one to define lazy lists. I would like to define a lazy list consisting of x, f x, f (f x), f (f (f x)), etc.
Based on comments in the module documentation, it appears that from_loop is the function I want:
"from_loop data next creates a (possibly infinite) lazy list from the successive results of applying next to data, then to the result, etc."
This description suggests that if I wanted a lazy list of non-negative integers, for example, I could define it like this:
let nat_nums = from_loop 0 (fun n -> n + 1)
However, this fails because the signature of from_loop is
'b -> ('b -> 'a * 'b) -> 'a LazyList.t
so the next function has signature ('b -> 'a * 'b). In utop, the error message underlines n + 1 and says
Error: This expression has type int but an expression was expected of type 'a * int
I don't understand what 'a is supposed to be. Why is the next function supposed to return a pair? Why is the type of the list supposed to be a 'a LazyList.t? Shouldn't the type of the elements be the same as the type of the argument to the next function? The description of the function doesn't make the answers clear to me.
In case it's helpful, my conception of what I'm trying to do comes from Clojure's iterate. In Clojure I could create the above definition like this:
(def nat-nums (iterate (fn [n] (+ n 1)) 0))
The function passed to from_loop has to return a pair. The first element of the pair is the value you want to return. The second element of the pair is the state required to calculate the next element later on.
Your code:
(fun n -> n + 1)
Just calculates the next element of the lazy list, it doesn't return the state required for the next call. Something like this is what is wanted:
(fun n -> (n, n + 1))
(This will return a list starting with 0, which I think is what you want.)
This formulation is more flexible than your clojure example, because it allows you to maintain arbitrary state distinct from the values returned. The state is of type 'b in the type you give for from_loop.
I don't have Batteries right now, so I can't try this out. But I think it's correct based on the types.
It turns out that the function that I really wanted was LazyList.seq, not from_loop. While from_loop has its uses, seq is simpler and does what I wanted. The only trick is that you have to provide a third argument which is a termination test that returns false when the list should end. I wanted an infinite list. One can create that using use a termination function that always returns true:
let nat_nums = seq 0 (fun n -> n + 1) (fun _ -> true);;
LazyList.to_list (LazyList.take 8 nat_nums);;
- : int list = [0; 1; 2; 3; 4; 5; 6; 7]
Related
I want to create a non-recursive function for my minimum
but I have some troubles with it
Can you help me please.
`let min_list lst=
let n=list.length lst ;;
let a=list.nth lst ;;
for i = 1 to n-1 ;;
let b=list.nth lst i;;
if a >b then a=b lst done ;;`
Honesly,It's difficult with non recursive fonction.So this is just for learning.I still have erreur in ligne 6
let min_list lst=
let a=List.hd lst in
let n=List.length lst in
for j =1 to n-1 do
let b=List.nth lst j in
if a > b then (let a=b) done ;;
Thank you it's useful It help me a lot .I have one other question what the difference between this
let min_array a =
let min =ref (List.hd a) in
for i = 1 to List.length a -1 do
if List.nth a i < !min then min := List.nth a i
done;
!min;;
print_int (min_array [ 10 ; 5 ; 7 ; 8 ; 12 ]);;
and
let min_array a =
let min =ref (List.hd a) in
for i = 1 to List.length a -1 do
if ref (List.nth a i) < min then min := List.nth a i
done;
!min;;
print_int (min_array [ 10 ; 5 ; 7 ; 8 ; 12 ]);;
It's the same ?I think
Why don't you want to use a recursive function ?
Liste are made to be crossed by recursive function. Everytime you use List.nth l n Ocaml has to cross n values until he found the nth element.
In Ocaml you can't change variable value as you do in other languages. You want a to be a ref.
Also your function won't return anything you'll have to put a !a between the done and the ;;. There will be a ! Because a will be a ref.
But if you want to practice use arrays instead because what you do here is in complexity O(n²) instead of O(n).
As said in the answer from Butanium, this kind of non-recursive function might be more relevant with arrays. And to work with mutable values, you need to use a reference.
A solution might then be something like (without dealing with case of an empty array):
let min_array a =
let min = ref a.(0) in
for i = 1 to Array.length a -1 do
if a.(i) < !min then min := a.(i)
done;
!min
The last line is important here, because it gets the value to be returned by the function.
Can then be used like that:
# min_array [| 10 ; 5 ; 7 ; 8 ; 12 |];;
- : int = 5
If you really do want to use lists instead of arrays, just use List.nth a i instead of a.(i) and List.length instead of Array.length.
Edit after question update
As Shawn and Jeffrey Scofield said in their respective comment, you should try to understand a bit better OCaml's syntax. And please don't use ;; in your programs, just keep it for the REPL.
As described in the documentation,
ref returns a fresh reference containing the given value.
Which means that when you write ref (List.nth a i) < min,
you create a fresh reference containing the i-th value the list, then compare it to min (which is also a reference). Luckily, mutable structures are compared by contents, which means that OCaml will access to your fresh reference's value, then access to min's value, and compare them. Thus, it will produce the same result as the direct comparison List.nth a i < !min, with a bit of useless memory allocation/access.
You can do this quite concisely by taking advantage of some features of the OCaml stdlib:
(* 'a list -> 'a option *)
let min_list l =
if List.length l > 0 then
Some (List.fold_left min (List.hd l) l)
else
None
Thanks to the min built-in, this works for lists of any type.
e.g. in a utop shell we can see:
min_list [99; 33; -1];;
- : int option = Some (-1)
min_list [99.1; 33.2; -1.3];;
- : float option = Some (-1.3)
min_list ["z"; "b"; "k"];;
- : string option = Some "b"
Explanation
First we recognise that the list may be empty, in which case we cannot return a meaningful value. This implies the function should return an option type, so either Some <value> or None.
Next we can use List.fold_left to iterate through the list.
Unfortunately the docs for List.fold_left are almost completely unhelpful:
val fold_left : ('a -> 'b -> 'a) -> 'a -> 'b list -> 'a
fold_left f init [b1; ...; bn] is f (... (f (f init b1) b2) ...) bn.
It's as if they assume that if you're using OCaml you're already an elite master of functional programming, who naturally knows what a "fold left" does.
I'm not an elite master of functional programming, but I've been around long enough to know that fold_left is basically the same as the reduce function in Python.
It's a function that iterates through a list, applies a function to each value as it goes, and returns a single value.
So we can start to make sense of the signature of fold_left...
It takes three arguments:
The first arg, f, is a function which itself takes two args - the first or 'left' arg is the 'accumulated' value, and the second arg is the current value from the list as we iterate through. Whatever value you return from this function will be passed back into it as the left 'accumulated' value on the next iteration. When the list is exhausted the accumulated value will be returned from fold_left.
The second arg, init is an initial value. It is passed to f as the left 'accumulated' arg in the first step, when nothing has been otherwise accumulated yet.
Third arg is our list of values
So when we return:
Some (List.fold_left min (List.hd l) l)
...we are passing the min function as f and (List.hd l) as init.
List.hd l just returns the first element of the list l. We could use any element from the list as an initial value, but List.hd exists and gives us the first.
So fold_left is going to iterate through the list and f will return min <accumulated> <current>. So at each iteration step the accumulated value passed forward is the lowest value seen so far.
Non-recursive?
I did wonder if perhaps the fold_left method does not count as non-recursive somehow, since no one else had suggested it. Even though we have not used let rec anywhere, maybe somewhere internally it is secretly recursive?
For fun I decided to try writing the reduce/fold function from scratch:
let reduce f init l =
let acc = ref init in
List.iter (fun el -> acc := f !acc el) l;
!acc
(* we can directly substitute `reduce` for `List.fold_left` *)
let min_list l =
if List.length l > 0 then
Some (reduce min (List.hd l) l)
else
None
...again, no let rec needed so I guess it counts as non-recursive.
I am trying to implement map using List.fold_right in OCaml, in which my function has the same signature and behaviour as List.map. I am not allowed to use pattern matching or any other list functions.
Example:
SIGNATURE: fold_map: ('a -> 'b) -> 'b list -> 'b list = <fun>
EXAMPLE: fold_map (fun x -> x + 1) [1; 2; 3; 4; 5] = [2; 3; 4; 5; 6]
My attempt:
let fold_map f =
List.fold_right(fun x acc -> (f x) :: acc) []
This type checks, however, when I run my function on the example, it returns the original list. I am not sure why this is happening, so any clarification would be appreciated.
The problem is taken from the forums of Coursera's programming languages course, which is in SML.
Check the List.fold_right documentation. It first takes the list to fold on and then the initial value. Therefore, your code is folding over the empty list and immediately returns the initial value, which is the list that you really want to fold over.
I have a datatype in this way
datatype 'a bin_tree =
Leaf of 'a
| Node of 'a bin_tree (* left tree *)
* int (* size of left tree *)
* int (* size of right tree *)
* 'a bin_tree (* right tree *)
so an example for correct tree would be:
val tree1 =
Node(Node(Node(Leaf 47, 1, 1, Leaf 38),
2,1,
Leaf 55),
3,2,
Node(Leaf 27, 1, 1, Leaf 96))
and an example for violating tree would be
val tree1false =
Node(Node(Node(Leaf 47, 1, 1, Leaf 38),
2,1,
Leaf 55),
4,2,
Node(Leaf 27, 1, 1, Leaf 96))
How can I write a predicate test such that
- test tree1;
val it = true : bool
- test tree1false;
val it = false : bool
This is a recursive problem. Before solving recursive problems on trees, it is a good idea to have a firm grasp on recursion on lists. You could say that trees are generalisations of lists, or that lists are special-cases of trees: lists have one tail, trees can have any number of tails depending on the type of tree. So here is how you could reconstruct and solve the problem using lists:
If, instead of the typical list definition, you have a list that also memoizes its own length:
(* datatype 'a list = [] | :: of 'a * 'a list *)
datatype 'a lenlist = Nil | Cons of int * 'a * 'a lenlist
Then you can test that the stored length is in accordance with the actual number of values.
I'll start by creating a function that counts to illustrate the part of the function that performs recursion:
(* For regular built-in lists *)
fun count0 [] = 0
| count0 (x::xs) = 1 + count0 xs
(* Counting the memoized list type disregarding the n *)
fun count1 Nil = 0
| count1 (Cons (n, x, xs)) = 1 + count1 xs
The next part is that I'd like, in each recursive step, to test that the stored number n is also in accordance with the actual counting. What is the return type of this function? Well, the test function that you want should be 'a lenlist -> bool and the count function that I made is 'a lenlist -> int.
I will suggest that you make a testcount that kinda does both. But you can do so in many ways, e.g. by giving it "extra arguments", or by giving it "extra return values". I will demonstrate both, just to show that sometimes one is better than the other and experience will tell you which.
Here is a val testcount1 : 'a lenlist -> bool * int function:
fun testcount1 Nil = (true, 0)
| testcount1 (Cons (n, x, xs)) =
let val (good_so_far, m) = testcount1 xs
val still_good = good_so_far andalso n = m + 1
in (still_good, m + 1)
end
val goodList = Cons (4, #"c", Cons (3, #"o", Cons (2, #"o", Cons (1, #"l", Nil))))
val badList = Cons (3, #"d", Cons (2, #"e", Cons (1, #"r", Cons (0, #"p", Nil))))
Testing this,
- testcount1 goodList;
> val it = (true, 4) : bool * int
- testcount1 badList;
> val it = (false, 4) : bool * int
This shows that testcount1 returns whether the numbers add up and the list's actual length, which was necessary during recursion to test that the numbers add up in each step, but in the end is no longer necessary. You could wrap this testcount function up in a simpler test function that only cares about the bool:
fun test xs = #1 (testcount1 xs)
Here is another way to go about: There is something not so satisfying with the way testcount1 recurses. It keeps calculating the m + 1s even though it is able to determine that a list (e.g. at Cons (0, #"p", Nil)) is broken.
Here is an alternate val testcount2 : 'a lenlist * int -> bool that stores a number in an extra argument instead:
fun testcount2 (Nil, 0) = true
| testcount2 (Nil, _) = false
| testcount2 (Cons (n, x, xs), m) =
n = m andalso testcount2 (xs, m - 1)
This seems a lot neater to me: The function is tail-recursive, and it stops immediately when it senses that something is fishy. So it doesn't need to traverse the entire list if it's broken at the head. The downside is that it needs to know the length, which we don't know yet. But we can compensate by assuming that whatever is advertised is correct until it's clearly the case, or not.
Testing this function, you need to not only give it a goodList or a badList but also an m:
- testcount2 (goodList, 4);
> val it = true : bool
- testcount2 (badList, 4);
> val it = false : bool
- testcount2 (badList, 3);
> val it = false : bool
It's important that this function doesn't just compare n = m, since in badList, that'd result in agreeing that badList is 3 elements long, since n = m is true for each iteration in all Cons cases. This is helped in the two Nil cases that require us to have reached 0 and not e.g. ~1 as is the case for badList.
This function can also be wrapped inside test to hide the fact that we feed it an extra argument derived from the 'a lenlist itself:
fun size Nil = 0
| size (Cons (n, _, _)) = n
fun test xs = testcount2 (xs, size xs)
Some morals so far:
Sometimes it is necessary to create helper functions to solve your initial problem.
Those helper functions are not restricted to have the same type signature as the function you deliver (whether this is for an exercise in school, or for an external API/library).
Sometimes it helps to extend the type that your function returns.
Sometimes it helps to extend the arguments of your functions.
Just because your task is "Write a function foo -> bar", this does not mean that you cannot create this by composing functions that return a great deal more or less than foo or bar.
Now, for some hints for solving this on binary trees:
Repeating the data type,
datatype 'a bin_tree =
Leaf of 'a
| Node of 'a bin_tree (* left tree *)
* int (* size of left tree *)
* int (* size of right tree *)
* 'a bin_tree (* right tree *)
You can start by constructing a skeleton for your function based on the ideas above:
fun testcount3 (Leaf x, ...) = ...
| testcount3 (Leaf x, ...) = ...
| testcount3 (Node (left, leftC, rightC, right), ...) = ...
I've embedded som hints here:
Your solution should probably contain pattern matches against Leaf x and Node (left, leftC, rightC, right). And given the "extra argument" type of solution (which at least proved nice for lists, but we'll see) needed two Leaf x cases. Why was that?
If, in the case of lists, the "extra argument" m represents the expected length of the list, then what would an "extra argument" represent in the case of trees? You can't say "it's the length of the list", since it's a tree. How do you capture the part where a tree branches?
If this is still too difficult, consider solving the problem for lists without copy-pasting. That is, you're allowed to look at the solutions in this answer (but try not to), but you're not allowed to copy-paste code. You have to type it if you want to copy it.
As a start, try to define the helper function size that was used to produce test from testcount2, but for trees. So maybe call it sizeTree to avoid the name overlap, but other than that, try and make it resemble. Here's a skeleton:
fun sizeTree (Leaf x) = ...
| sizeTree (Node (left, leftC, rightC, right)) = ...
Sticking testcount3 and sizeTree together, once written, should be easy as tau.
I'm still trying to understand how fold_left exactly works. Does it iterate through the list like List.iter? Or is there just something else wrong with my code? I'm thinking that e is the element in the list (so it's a tuple) and fst e takes the first element of the tuple and snd e takes the second element in the tuple.
let rec pow x n =
if n < 0 then
0
else if n = 0 then
1
else
x * pow x (n - 1);;
let polynomial lst = function
| x -> List.fold_left (fun e -> (fst e) * (pow x (snd e))) 1 lst;;
lst is a list of tuples where each tuple has two integers and makes a polynomial function, so polynomial is supposed to return a function. So an example of what should happen is this
# let f = polynomial [3, 3; -2, 1; 5, 0];;
val f : int -> int = <fun>
# f 2;; (* f is the polynomial function f(x) = 3x^3 + (-2)x + 5 *)
- : int = 25
But I get this error message
"Error: This expression has type int but an expression was expected of type 'a -> int * int".
List.fold_left indeed iterates over a list, passing the value from one call to another, which basically works like a bucket brigade, with only one bucket, where on each iteration you can look into the bucket, take whatever is there and put something new.
More formally, fold_left f init elements has type
val fold_left : ('a -> 'b -> 'a) -> 'a -> 'b list -> 'a
and it takes three arguments, the function f, the initial value init, and a list of elements. The function f is called for each element x of elements as f acc x, where acc is either init if x is the first element of the list or a result returned by the previous invocation of f. Back to our analogy, it is either the initial empty bucket or a bucket passed from the previous call in the chain.
In your case, the role of the bucket is the final sum of all terms. Initially, it is empty, then each new term computes (fst e) * (pow x (snd e)) and adds it to the bucket so that at the end you will have the sum of all terms,
let polynomial coeffs x =
List.fold_left (fun sum (k,r) -> sum + k * pow x r) 0 coeffs
Note, that instead of using fst and snd to access the elements of the pair, I deconstructed the tuple directly in the parameter list. This makes code easier to understand and shorter.
The function, that is applied on each step takes two arguments, sum is the bucket (it is often called the "accumulator") and the element of the list, which is a pair (k,r) in our case. We multiply k by the value of the x variable raised to the power r and then we add the result to the accumulator.
For people with an imperative mindset the following pseudocode1 might be more insightful than the bucket brigade analogy:
def fold_left(user_func, init, elements):
acc = init
for elt in elts:
acc = user_func(acc, elt)
return acc
1) Any resemblance to Python is purely coincidental :)
I am attempting to make a function such that it outputs a list that removes adjacent duplicates
let rec rem_adj_duplicates l =
let rec utn l n = match l with
[] -> n
| (x :: y :: xs) -> if (x != y) then utn (y::xs) (n::x)
else utn (y::xs) n
in utn l []
I get the following error:
Error: This expression has type 'a list
but an expression was expected of type 'a
The type variable 'a occurs inside 'a list
Why does n want to be type 'a instead of 'a list?
You have this:
if (x != y) then
utn (y::xs) (n::x)
else
utn (y::xs) n
In the first call you have n :: x, which says that x is a list and n is a non-list. Furthermore it says that the second argument to utn is a list. In the next call you have just n, which must be of the same type as the other call. Thus, n shows up as both a list and a non-list.
Possibly you wanted to have x :: n rather than n :: x?
Update
You say you're not allowed to use the # operator. This is actually reasonable, as using it in the way you wanted is not a good way to solve the problem. (Adding things one at a time to the end of a list is slow, i.e., quadratic time in the length of the list.)
I may be giving away too much, but a common way to solve this problem is to build the list in reverse order and reverse it at the end. This takes only linear time.