/ \ / \
1 2 2 3
-have the same fringe: `[1;2;3]`. We also asked you to write a function that determined when two trees have the same fringe. The way you approached that back then was to enumerate each tree's fringe, and then compare the two lists for equality. Today, and then again in a later class, we'll encounter new ways to approach the problem of determining when two trees have the same fringe.
+have the same fringe: `[1; 2; 3]`. We also asked you to write a function that determined when two trees have the same fringe. The way you approached that back then was to enumerate each tree's fringe, and then compare the two lists for equality. Today, and then again in a later class, we'll encounter new ways to approach the problem of determining when two trees have the same fringe.
-Supposing you did work out an implementation of the tree zipper, then one way to determine whether two trees have the same fringe would be: go downwards (and leftwards) in each tree as far as possible. Compare the targetted leaves. If they're different, stop because the trees have different fringes. If they're the same, then for each tree, move rightward if possible; if it's not (because you're at the rightmost position in a sibling list), more upwards then try again to move rightwards. Repeat until you are able to move rightwards. Once you do move rightwards, go downwards (and leftwards) as far as possible. Then you'll be targetted on the next leaf in the tree's fringe. The operations it takes to get to "the next leaf" may be different for the two trees. For example, in these trees:
+Supposing you did work out an implementation of the tree zipper, then one way to determine whether two trees have the same fringe would be: go downwards (and leftwards) in each tree as far as possible. Compare the targetted leaves. If they're different, stop because the trees have different fringes. If they're the same, then for each tree, move rightward if possible; if it's not (because you're at the rightmost position in a sibling list), move upwards then try again to move rightwards. Repeat until you are able to move rightwards. Once you do move rightwards, go downwards (and leftwards) as far as possible. Then you'll be targetted on the next leaf in the tree's fringe. The operations it takes to get to "the next leaf" may be different for the two trees. For example, in these trees:
. .
/ \ / \
/ \ / \
1 2 2 3
-you won't move upwards at the same steps. Keep comparing "the next leafs" until they are different, or you exhaust the leafs of only one of the trees (then again the trees have different fringes), or you exhaust the leafs of both trees at the same time, without having found leafs with different labels. In this last case, the trees have the same fringe.
+you won't move upwards at the same steps. Keep comparing "the next leaves" until they are different, or you exhaust the leaves of only one of the trees (then again the trees have different fringes), or you exhaust the leaves of both trees at the same time, without having found leaves with different labels. In this last case, the trees have the same fringe.
If your trees are very big---say, millions of leaves---you can imagine how this would be quicker and more memory-efficient than traversing each tree to construct a list of its fringe, and then comparing the two lists so built to see if they're equal. For one thing, the zipper method can abort early if the fringes diverge early, without needing to traverse or build a list containing the rest of each tree's fringe.
records let you attach descriptive labels to the components of the tuple:
# type blah_record = { height : int; weight : int; char_tester : char -> bool };;
- # let b2 = { height = 1; weight = 2; char_tester = fun c -> c = 'M' };;
+ # let b2 = { height = 1; weight = 2; char_tester = (fun c -> c = 'M') };;
val b2 : blah_record = {height = 1; weight = 2; char_tester = <fun>}
# let b3 = { height = 1; char_tester = (fun c -> c = 'K'); weight = 3 };; (* also works *)
val b3 : blah_record = {height = 1; weight = 3; char_tester = <fun>}
match test with
| {height = h; weight = w; char_tester = test} ->
- (* go on to use h, w, and test ... *)
+ (* same as preceding *)
Anyway, using record types, we might define the tree zipper interface like so:
Using these fringe enumerators, we can write our `same_fringe` function like this:
let same_fringe (t1 : 'a tree) (t2 : 'a tree) : bool =
- let next1 = make_fringe_enumerator t1
- in let next2 = make_fringe_enumerator t2
- in let rec loop () : bool =
- match next1 (), next2 () with
- | Some a, Some b when a = b -> loop ()
- | None, None -> true
- | _ -> false
- in loop ()
- ;;
+ let next1 = make_fringe_enumerator t1
+ in let next2 = make_fringe_enumerator t2
+ in let rec loop () : bool =
+ match next1 (), next2 () with
+ | Some a, Some b when a = b -> loop ()
+ | None, None -> true
+ | _ -> false
+ in loop ()
+ ;;
The auxiliary `loop` function will keep calling itself recursively until a difference in the fringes has manifested itself---either because one fringe is exhausted before the other, or because the next leaves in the two fringes have different labels. If we get to the end of both fringes at the same time (`next1 (), next2 ()` matches the pattern `None, None`) then we've established that the trees do have the same fringe.
With cooperative threads, one typically yields control to the thread, and then back again to the main program, multiple times. Here's the pattern in which that happens in our `same_fringe` function:
- main program next1 thread next2 thread
- ------------ ------------ ------------
+ main program next1 thread next2 thread
+ ------------ ------------ ------------
start next1
- (paused) starting
- (paused) calculate first leaf
- (paused) <--- return it
- start next2 (paused) starting
- (paused) (paused) calculate first leaf
- (paused) (paused) <-- return it
- compare leaves (paused) (paused)
- call loop again (paused) (paused)
- call next1 again (paused) (paused)
- (paused) calculate next leaf (paused)
- (paused) <-- return it (paused)
+ (paused) starting
+ (paused) calculate first leaf
+ (paused) <--- return it
+ start next2 (paused) starting
+ (paused) (paused) calculate first leaf
+ (paused) (paused) <-- return it
+ compare leaves (paused) (paused)
+ call loop again (paused) (paused)
+ call next1 again (paused) (paused)
+ (paused) calculate next leaf (paused)
+ (paused) <-- return it (paused)
... and so on ...
If you want to read more about these kinds of threads, here are some links:
# let lst = [1; 2] in
"a" :: lst;;
Error: This expression has type int list
- but an expression was expected of type string list
+ but an expression was expected of type string list
But you may also have encountered other kinds of error, that arise while your program is running. For example:
let nth l n =
if n < 0 then invalid_arg "List.nth" else
let rec nth_aux l n =
- match l with
- | [] -> failwith "nth"
- | a::l -> if n = 0 then a else nth_aux l (n-1)
+ match l with
+ | [] -> failwith "nth"
+ | a::l -> if n = 0 then a else nth_aux l (n-1)
in nth_aux l n
Notice the two clauses `invalid_arg "List.nth"` and `failwith "nth"`. These are two helper functions which are shorthand for:
(if x = 1 then 10
else if x = 2 then raise (Failure "two")
else raise (Failure "three")
- ) + 100
+ ) + 100
with Failure "two" -> 20
;;
val foo : int -> int = <fun>
# foo 1;;
- - : int = 10
+ - : int = 110
# foo 2;;
- : int = 20
# foo 3;;
# try
try
(raise (Failure "blah")
- ) + 100
+ ) + 100
with Failure "fooey" -> 10
with Failure "blah" -> 20;;
- : int = 20
# let foo b x =
try
(b x
- ) + 100
+ ) + 100
with Failure "blah" -> 20
in let bar x =
raise (Failure "blah")
try begin
(if x = 1 then 10
else abort 20
- ) + 100
+ ) + 100
end
;;
if (x == 1) then
value = 10
else
- return 20 -- return early
+ return 20 -- abort early
end
return value + 100 -- in Lua, a function's normal value
-- must always also be explicitly returned
try begin
(if x = 1 then 10
else abort 20
- ) + 100
+ ) + 100
end
in (foo 2) + 1;;
and we can think of the code starting with `let foo_result = ...` as a function, with the box being its parameter, like this:
fun box ->
- let foo_result = box
- in (foo_result) + 1000
+ let foo_result = box
+ in (foo_result) + 1000
That function is our "snapshot". Normally what happens is that code *inside* the box delivers up a value, and that value gets supplied as an argument to the snapshot-function just described. That is, our code is essentially working like this:
let x = 2
in let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
+ let foo_result = box
+ in (foo_result) + 1000
in let value =
- (if x = 1 then 10
- else ... (* we'll come back to this part *)
- ) + 100
+ (if x = 1 then 10
+ else ... (* we'll come back to this part *)
+ ) + 100
in shapshot value;;
But now how should the `abort 20` part, that we ellided here, work? What should happen when we try to evaluate that?
let x = 2
in let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
+ let foo_result = box
+ in (foo_result) + 1000
in let value =
- (if x = 1 then 10
- else snapshot 20
- ) + 100
+ (if x = 1 then 10
+ else snapshot 20
+ ) + 100
in shapshot value;;
Except that isn't quite right, yet---in this fragment, after the `snapshot 20` code is finished, we'd pick up again inside `let value = (...) + 100 in snapshot value`. That's not what we want. We don't want to pick up again there. We want instead to do this:
let x = 2
in let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
+ let foo_result = box
+ in (foo_result) + 1000
in let value =
- (if x = 1 then 10
- else snapshot 20 THEN STOP
- ) + 100
+ (if x = 1 then 10
+ else snapshot 20 THEN STOP
+ ) + 100
in shapshot value;;
We can get that by some further rearranging of the code:
let x = 2
in let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
+ let foo_result = box
+ in (foo_result) + 1000
in let continue_normally = fun from_value ->
- let value = from_value + 100
- in snapshot value
- in
- if x = 1 then continue_normally 10
- else snapshot 20;;
+ let value = from_value + 100
+ in snapshot value
+ in
+ if x = 1 then continue_normally 10
+ else snapshot 20;;
And this is indeed what is happening, at a fundamental level, when you use an expression like `abort 20`.
# open Delimcc;;
# let reset body = let p = new_prompt () in push_prompt p (body p);;
# let test_cps x =
- let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
- in let continue_normally = fun from_value ->
- let value = from_value + 100
- in snapshot value
- in if x = 1 then continue_normally 10
- else snapshot 20;;
+ let snapshot = fun box ->
+ let foo_result = box
+ in (foo_result) + 1000
+ in let continue_normally = fun from_value ->
+ let value = from_value + 100
+ in snapshot value
+ in if x = 1 then continue_normally 10
+ else snapshot 20;;
let foo x =
+===try begin================+
in (foo 2) + 1000;;
# let test_shift x =
- let foo x = reset(fun p () ->
- (shift p (fun k ->
- if x = 1 then k 10 else 20)
- ) + 100)
- in foo z + 1000;;
+ let foo x = reset(fun p () ->
+ (shift p (fun k ->
+ if x = 1 then k 10 else 20)
+ ) + 100)
+ in foo z + 1000;;
# test_cps 1;;
- : int = 1110
You can think of them as functions that represent "how the rest of the computation proposes to continue." Except that, once we're able to get our hands on those functions, we can do exotic and unwholesome things with them. Like use them to suspend and resume a thread. Or to abort from deep inside a sub-computation: one function might pass the command to abort *it* to a subfunction, so that the subfunction has the power to jump directly to the outside caller. Or a function might *return* its continuation function to the outside caller, giving *the outside caller* the ability to "abort" the function (the function that has already returned its value---so what should happen then?) Or we may call the same continuation function *multiple times* (what should happen then?). All of these weird and wonderful possibilities await us.
-The key idea behind working with continuations is that we're *inverting control*. In the fragment above, the code `(if x = 1 then ... else snapshot 20) + 100` which is written so as to supply a value to the outside context that we snapshotted itself *makes non-trivial use of* that snapshot. So it has to be able to refer to that snapshot; the snapshot has to somehow be available to our inner code as an *argument* or bound variable. That is: the cde that is *written* like it's supplying an argument to the outside context is instead *getting that context as its own argument*. He who is written as value-supplying slave is instead become the outer context's master.
+The key idea behind working with continuations is that we're *inverting control*. In the fragment above, the code `(if x = 1 then ... else snapshot 20) + 100`---which is written as if it were to supply a value to the outside context that we snapshotted---itself *makes non-trivial use of* that snapshot. So it has to be able to refer to that snapshot; the snapshot has to somehow be available to our inside-the-box code as an *argument* or bound variable. That is: the code that is *written* like it's supplying an argument to the outside context is instead *getting that context as its own argument*. He who is written as value-supplying slave is instead become the outer context's master.
In fact you've already seen this several times this semester---recall how in our implementation of pairs in the untyped lambda-calculus, the handler who wanted to use the pair's components had *in the first place to be supplied to the pair as an argument*. So the exotica from the end of the seminar was already on the scene in some of our earliest steps. Recall also what we did with v2 and v5 lists. Version 5 lists were the ones that let us abort a fold early:
go back and re-read the material on "Aborting a Search Through a List" in [[Week4]].
-This inversion of control should also remind you of Montague's treatment of subject terms in ["The Proper Treatment of Quantification in Ordinary English"](http://www.blackwellpublishing.com/content/BPL_Images/Content_store/Sample_chapter/0631215417%5CPortner.pdf) (PTQ).
+This inversion of control should also remind you of Montague's treatment of determiner phrases in ["The Proper Treatment of Quantification in Ordinary English"](http://www.blackwellpublishing.com/content/BPL_Images/Content_store/Sample_chapter/0631215417%5CPortner.pdf) (PTQ).
A naive semantics for atomic sentences will say the subject term is of type `e`, and the predicate of type `e -> t`, and that the subject provides an argument to the function expressed by the predicate.
-Monatague proposed we instead take subject terms to be of type `(e -> t) -> t`, and that now it'd be the predicate (still of type `e -> t`) that provides an argument to the function expressed by the subject.
+Monatague proposed we instead take the subject term to be of type `(e -> t) -> t`, and that now it'd be the predicate (still of type `e -> t`) that provides an argument to the function expressed by the subject.
If all the subject did then was supply an `e` to the `e -> t` it receives as an argument, we wouldn't have gained anything we weren't already able to do. But of course, there are other things the subject can do with the `e -> t` it receives as an argument. For instance, it can check whether anything in the domain satisfies that `e -> t`; or whether most things do; and so on.
This inversion of who is the argument and who is the function receiving the argument is paradigmatic of working with continuations.
-Continuations come in many varieties. There are **undelimited continuations**, expressed in Scheme via `(call/cc (lambda (k) ...))` or the shorthand `(let/cc k ...)`. These capture "the entire rest of the computation." There are also **delimited continuations**, expressed in Scheme via `(reset ... (shift k ...) ...)` or `(prompt ... (control k ...) ...)` or any of several other operations. There are subtle differences between these that we won't be exploring in the seminar. Ken Shan has done amazing work exploring the relations of these operations to each other.
+Continuations come in many varieties. There are **undelimited continuations**, expressed in Scheme via `(call/cc (lambda (k) ...))` or the shorthand `(let/cc k ...)`. (`call/cc` is itself shorthand for `call-with-current-continuation`.) These capture "the entire rest of the computation." There are also **delimited continuations**, expressed in Scheme via `(reset ... (shift k ...) ...)` or `(prompt ... (control k ...) ...)` or any of several other operations. There are subtle differences between those that we won't be exploring in the seminar. Ken Shan has done amazing work exploring the relations of these operations to each other.
When working with continuations, it's easiest in the first place to write them out explicitly, the way that we explicitly wrote out the `snapshot` continuation when we transformed this:
let foo x =
try begin
(if x = 1 then 10
- else abort 20) + 100
+ else abort 20
+ ) + 100
end
in (foo 2) + 1;;
let x = 2
in let snapshot = fun box ->
- let foo_result = box
- in (foo_result) + 1000
- in let finish_value = fun start ->
- let value = start + 100
- in snapshot value
- in
- if x = 1 then finish_value 10
- else snapshot 20;;
+ let foo_result = box
+ in (foo_result) + 1000
+ in let continue_normally = fun from_value ->
+ let value = from_value + 100
+ in snapshot value
+ in
+ if x = 1 then continue_normally 10
+ else snapshot 20;;
Code written in the latter form is said to be written in **explicit continuation-passing style** or CPS. Later we'll talk about algorithms that mechanically convert an entire program into CPS.
-There are also different kinds of "syntactic sugar" we can use to hide the continuation plumbing. Of course we'll be talking about how to manipulate continuations **with a continuation monad.** We'll also talk about a style of working with continuations where they're **mostly implicit**, but special syntax allows us to distill the implicit continuaton into a first-class value (the `k` in `(let/cc k ...)` and `(shift k ...)`.
+There are also different kinds of "syntactic sugar" we can use to hide the continuation plumbing. Of course we'll be talking about how to manipulate continuations **with a Continuation monad.** We'll also talk about a style of working with continuations where they're **mostly implicit**, but special syntax allows us to distill the implicit continuaton into a first-class value (the `k` in `(let/cc k ...)` and `(shift k ...)`.
Various of the tools we've been introducing over the past weeks are inter-related. We saw coroutines implemented first with zippers; here we've talked in the abstract about their being implemented with continuations. Oleg says that "Zipper can be viewed as a delimited continuation reified as a data structure." Ken expresses the same idea in terms of a zipper being a "defunctionalized" continuation---that is, take something implemented as a function (a continuation) and implement the same thing as an inert data structure (a zipper).