Related
In Haskell, some lists are cyclic:
ones = 1 : ones
Others are not:
nums = [1..]
And then there are things like this:
more_ones = f 1 where f x = x : f x
This denotes the same value as ones, and certainly that value is a repeating sequence. But whether it's represented in memory as a cyclic data structure is doubtful. (An implementation could do so, but this answer explains that "it's unlikely that this will happen in practice".)
Suppose we take a Haskell implementation and hack into it a built-in function isCycle :: [a] -> Bool that examines the structure of the in-memory representation of the argument. It returns True if the list is physically cyclic and False if the argument is of finite length. Otherwise, it will fail to terminate. (I imagine "hacking it in" because it's impossible to write that function in Haskell.)
Would the existence of this function break any interesting properties of the language?
Would the existence of this function break any interesting properties of the language?
Yes it would. It would break referential transparency (see also the Wikipedia article). A Haskell expression can be always replaced by its value. In other words, it depends only on the passed arguments and nothing else. If we had
isCycle :: [a] -> Bool
as you propose, expressions using it would not satisfy this property any more. They could depend on the internal memory representation of values. In consequence, other laws would be violated. For example the identity law for Functor
fmap id === id
would not hold any more: You'd be able to distinguish between ones and fmap id ones, as the latter would be acyclic. And compiler optimizations such as applying the above law would not longer preserve program properties.
However another question would be having function
isCycleIO :: [a] -> IO Bool
as IO actions are allowed to examine and change anything.
A pure solution could be to have a data type that internally distinguishes the two:
import qualified Data.Foldable as F
data SmartList a = Cyclic [a] | Acyclic [a]
instance Functor SmartList where
fmap f (Cyclic xs) = Cyclic (map f xs)
fmap f (Acyclic xs) = Acyclic (map f xs)
instance F.Foldable SmartList where
foldr f z (Acyclic xs) = F.foldr f z xs
foldr f _ (Cyclic xs) = let r = F.foldr f r xs in r
Of course it wouldn't be able to recognize if a generic list is cyclic or not, but for many operations it'd be possible to preserve the knowledge of having Cyclic values.
In the general case, no you can't identify a cyclic list. However if the list is being generated by an unfold operation then you can. Data.List contains this:
unfoldr :: (b -> Maybe (a, b)) -> b -> [a]
The first argument is a function that takes a "state" argument of type "b" and may return an element of the list and a new state. The second argument is the initial state. "Nothing" means the list ends.
If the state ever recurs then the list will repeat from the point of the last state. So if we instead use a different unfold function that returns a list of (a, b) pairs we can inspect the state corresponding to each element. If the same state is seen twice then the list is cyclic. Of course this assumes that the state is an instance of Eq or something.
I have a function that will take and int and return its square root. However now i want to modify it so that it takes an array of integers and gives back an array with the square roots of the elements of the first array. I know Haskell does not use loops so how can this modification be done? Thanks.
intSquareRoot :: Int -> Int
intSquareRoot n = try n where
try i | i*i > n = try (i - 1)
| i*i <= n = i
Don't.
The idea of “looping through some collection”, putting each result in the corresponding slot of its input, is a somewhat trivial, extremely common pattern. Patterns are for OO programmers. In Haskell, when there's a pattern, we want to abstract over it, i.e. give it a simple name that we can always re-use without extra boilerplate.
This particular “pattern” is the functor operation1. For lists it's called
map :: (a->b) -> [a]->[b]
more generally (e.g. it'll also work with real arrays; lists aren't actually arrays),
class Functor f where
fmap :: (a->b) -> f a->f b
So instead of defining an extra function
intListSquareRoot :: [Int] -> [Int]
intListSquareRoot = ...
you simply use map intSquareRoot right where you wanted to use that function.
Of course, you could also define that “lifted” version of intSquareRoot,
intListSquareRoot = map intSquareRoot
but that gains you practically nothing over simply inlining the map call right where you need it.
If you insist
That said... it's of course valid to wonder how map itself works. Well, you can manually “loop” through a list by recursion:
map' :: (a->b) -> [a]->[b]
map' _ [] = []
map' f (x:xs) = f x : map' f xs
Now, you could inline your specific function here
intListSquareRoot' :: [Int] -> [Int]
intListSquareRoot' [] = []
intListSquareRoot' (x:xs) = intSquareRoot x : intListSquareRoot' xs
This is not only much more clunky and awkward than quickly inserting the map magic word, it will also often be slower: compilers such as GHC can make better optimisations when they work on higher-level concepts2 such as folds, than when they have to work again and again with manually defined recursion.
1Not to be confused what many C++ programmers call a “functor”. Haskell uses the word in the correct mathematical sense, which comes from category theory.
2This is why languages such as Matlab and APL actually achieve decent performance for special applications, although they are dynamically-typed, interpreted languages: they have this special case of “vector looping” hard-coded into their very syntax. (Unfortunately, this is pretty much the only thing they can do well...)
You can use map:
arraySquareRoot = map intSquareRoot
I was reading the paper authored by Simon Peyton Jones, et al. named “Playing by the Rules: Rewriting as a practical optimization technique in GHC”. In the second section, namely “The basic idea” they write:
Consider the familiar map function, that applies a function to each element of a list. Written in Haskell, map looks like this:
map f [] = []
map f (x:xs) = f x : map f xs
Now suppose that the compiler encounters the following call of map:
map f (map g xs)
We know that this expression is equivalent to
map (f . g) xs
(where “.” is function composition), and we know that the latter expression is more efficient than the former because there is no intermediate list. But the compiler has no such knowledge.
One possible rejoinder is that the compiler should be smarter --- but the programmer will always know things that the compiler cannot figure out. Another suggestion is this: allow the programmer to communicate such knowledge directly to the compiler. That is the direction we explore here.
My question is, why can't we make the compiler smarter? The authors say that “but the programmer will always know things that the compiler cannot figure out”. However, that's not a valid answer because the compiler can indeed figure out that map f (map g xs) is equivalent to map (f . g) xs, and here is how:
map f (map g xs)
map g xs unifies with map f [] = [].
Hence map g [] = [].
map f (map g []) = map f [].
map f [] unifies with map f [] = [].
Hence map f (map g []) = [].
map g xs unifies with map f (x:xs) = f x : map f xs.
Hence map g (x:xs) = g x : map g xs.
map f (map g (x:xs)) = map f (g x : map g xs).
map f (g x : map g xs) unifies with map f (x:xs) = f x : map f xs.
Hence map f (map g (x:xs)) = f (g x) : map f (map g xs).
Hence we now have the rules:
map f (map g []) = []
map f (map g (x:xs)) = f (g x) : map f (map g xs)
As you can see f (g x) is just (f . g) and map f (map g xs) is being called recursively. This is exactly the definition of map (f . g) xs. The algorithm for this automatic conversion seems to be pretty simple. So why not implement this instead of rewriting rules?
Aggressive inlining can derive many of the equalities that rewrite rules are short-hand for.
The differences is that inlining is "blind", so you don't know in advance if the result will be better or worse, or even if it will terminate.
Rewrite rules, however, can do completely non-obvious things, based on much higher level facts about the program. Think of rewrite rules as adding new axioms to the optimizer. By adding these you have a richer rule set to apply, making complicated optimizations easier to apply.
Stream fusion, for example, changes the data type representation. This cannot be expressed through inlining, as it involves a representation type change (we reframe the optimization problem in terms of the Stream ADT). Easy to state in rewrite rules, impossible with inlining alone.
Something in that direction was investigated in a Bachelor’s thesis of Johannes Bader, a student of mine: Finding Equations in Functional Programs (PDF file).
To some degree it is certainly possible, but
it is quite tricky. Finding such equations is in a sense as hard as finding proofs in a theorem proofer, and
it is not often very useful, because it tends to find equations that the programmer would rarely write directly.
It is however useful to clean up after other transformations such as inlining and various form of fusion.
This could be viewed as a balance between balancing expectations in the specific case, and balancing them in the general case. This balance can generate funny situations where you can know how to make something faster, but it is better for the language in general if you don't.
In the specific case of maps in the structure you give, the computer could find optimizations. However, what about related structures? What if the function isn't map? What if there's an additional layer of indirection, such as a function that returns map. In those cases, the compiler cannot optimize easily. This is the general case problem.
How if you do optimize the special case, one of two outcomes occurs
Nobody relies on it, because they aren't sure if it is there or not. In this case, articles like the one you quote get written
People do start relying on it, and now every developer is forced to remember "maps done in this configuration get automatically converted to the fast version for me, but if I do it in this configuration I don't.' This starts to manipulate the way people use the language, and can actually reduce readability!
Given the need for developers to think about such optimizations in the general case, we expect to see developers doing these optimizations in the simple case, decreasing the need to for the optimization in the first place!
Now, if it turns out that the particular case you are interested accounts for something massive like 2% of the world codebase in Haskell, there would be a much stronger argument for applying your special-case optimization.
Here's a simple, barebones example of how the code that I'm trying to do would look in C++.
while (state == true) {
a = function1();
b = function2();
state = function3();
}
In the program I'm working on, I have some functions that I need to loop through until bool state equals false (or until one of the variables, let's say variable b, equals 0).
How would this code be done in Haskell? I've searched through here, Google, and even Bing and haven't been able to find any clear, straight forward explanations on how to do repetitive actions with functions.
Any help would be appreciated.
Taking Daniels comment into account, it could look something like this:
f = loop init_a init_b true
where
loop a b True = loop a' b' (fun3 a' b')
where
a' = fun1 ....
b' = fun2 .....
loop a b False = (a,b)
Well, here's a suggestion of how to map the concepts here:
A C++ loop is some form of list operation in Haskell.
One iteration of the loop = handling one element of the list.
Looping until a certain condition becomes true = base case of a function that recurses on a list.
But there is something that is critically different between imperative loops and functional list functions: loops describe how to iterate; higher-order list functions describe the structure of the computation. So for example, map f [a0, a1, ..., an] can be described by this diagram:
[a0, a1, ..., an]
| | |
f f f
| | |
v v v
[f a0, f a1, ..., f an]
Note that this describes how the result is related to the arguments f and [a0, a1, ..., an], not how the iteration is performed step by step.
Likewise, foldr f z [a0, a1, ..., an] corresponds to this:
f a0 (f a1 (... (f an z)))
filter doesn't quite lend itself to diagramming, but it's easy to state many rules that it satisfies:
length (filter pred xs) <= length xs
For every element x of filter pred xs, pred x is True.
If x is an element of filter pred xs, then x is an element of xs
If x is not an element of xs, then x is not an element of filter pred xs
If x appears before x' in filter pred xs, then x appears before x' in xs
If x appears before x' in xs, and both x and x' appear in filter pred xs, then x appears before x' in filter pred xs
In a classic imperative program, all three of these cases are written as loops, and the difference between them comes down to what the loop body does. Functional programming, on the contrary, insists that this sort of structural pattern does not belong in "loop bodies" (the functions f and pred in these examples); rather, these patterns are best abstracted out into higher-order functions like map, foldr and filter. Thus, every time you see one of these list functions you instantly know some important facts about how the arguments and the result are related, without having to read any code; whereas in a typical imperative program, you must read the bodies of loops to figure this stuff out.
So the real answer to your question is that it's impossible to offer an idiomatic translation of an imperative loop into functional terms without knowing what the loop body is doing—what are the preconditions supposed to be before the loop runs, and what the postconditions are supposed to be when the loop finishes. Because that loop body that you only described vaguely is going to determine what the structure of the computation is, and different such structures will call for different higher-order functions in Haskell.
First of all, let's think about a few things.
Does function1 have side effects?
Does function2 have side effects?
Does function3 have side effects?
The answer to all of these is a resoundingly obvious YES, because they take no inputs, and presumably there are circumstances which cause you to go around the while loop more than once (rather than def function3(): return false). Now let's remodel these functions with explicit state.
s = initialState
sentinel = true
while(sentinel):
a,b,s,sentinel = function1(a,b,s,sentinel)
a,b,s,sentinel = function2(a,b,s,sentinel)
a,b,s,sentinel = function3(a,b,s,sentinel)
return a,b,s
Well that's rather ugly. We know absolutely nothing about what inputs each function draws from, nor do we know anything about how these functions might affect the variables a, b, and sentinel, nor "any other state" which I have simply modeled as s.
So let's make a few assumptions. Firstly, I am going to assume that these functions do not directly depend on nor affect in any way the values of a, b, and sentinel. They might, however, change the "other state". So here's what we get:
s = initState
sentinel = true
while (sentinel):
a,s2 = function1(s)
b,s3 = function2(s2)
sentinel,s4 = function(s3)
s = s4
return a,b,s
Notice I've used temporary variables s2, s3, and s4 to indicate the changes that the "other state" goes through. Haskell time. We need a control function to behave like a while loop.
myWhile :: s -- an initial state
-> (s -> (Bool, a, s)) -- given a state, produces a sentinel, a current result, and the next state
-> (a, s) -- the result, plus resultant state
myWhile s f = case f s of
(False, a, s') -> (a, s')
(True, _, s') -> myWhile s' f
Now how would one use such a function? Well, given we have the functions:
function1 :: MyState -> (AType, MyState)
function2 :: MyState -> (BType, MyState)
function3 :: MyState -> (Bool, MyState)
We would construct the desired code as follows:
thatCodeBlockWeAreTryingToSimulate :: MyState -> ((AType, BType), MyState)
thatCodeBlockWeAreTryingToSimulate initState = myWhile initState f
where f :: MyState -> (Bool, (AType, BType), MyState)
f s = let (a, s2) = function1 s
(b, s3) = function2 s2
(sentinel, s4) = function3 s3
in (sentinel, (a, b), s4)
Notice how similar this is to the non-ugly python-like code given above.
You can verify that the code I have presented is well-typed by adding function1 = undefined etc for the three functions, as well as the following at the top of the file:
{-# LANGUAGE EmptyDataDecls #-}
data MyState
data AType
data BType
So the takeaway message is this: in Haskell, you must explicitly model the changes in state. You can use the "State Monad" to make things a little prettier, but you should first understand the idea of passing state around.
Lets take a look at your C++ loop:
while (state == true) {
a = function1();
b = function2();
state = function3();
}
Haskell is a pure functional language, so it won't fight us as much (and the resulting code will be more useful, both in itself and as an exercise to learn Haskell) if we try to do this without side effects, and without using monads to make it look like we're using side effects either.
Lets start with this structure
while (state == true) {
<<do stuff that updates state>>
}
In Haskell we're obviously not going to be checking a variable against true as the loop condition, because it can't change its value[1] and we'd either evaluate the loop body forever or never. So instead, we'll want to be evaluating a function that returns a boolean value on some argument:
while (check something == True) {
<<do stuff that updates state>>
}
Well, now we don't have a state variable, so that "do stuff that updates state" is looking pretty pointless. And we don't have a something to pass to check. Lets think about this a bit more. We want the something to be checked to depend on what the "do stuff" bit is doing. We don't have side effects, so that means something has to be (or be derived from) returned from the "do stuff". "do stuff" also needs to take something that varies as an argument, or it'll just keep returning the same thing forever, which is also pointless. We also need to return a value out all this, otherwise we're just burning CPU cycles (again, with no side effects there's no point running a function if we don't use its output in some way, and there's even less point running a function repeatedly if we never use its output).
So how about something like this:
while check func state =
let next_state = func state in
if check next_state
then while check func next_state
else next_state
Lets try it in GHCi:
*Main> while (<20) (+1) 0
20
This is the result of applying (+1) repeatedly while the result is less than 20, starting from 0.
*Main> while ((<20) . length) (++ "zob") ""
"zobzobzobzobzobzobzob"
This is the result of concatenating "zob" repeatedly while the result's length is less than 20, starting from the empty string.
So you can see I've defined a function that is (sort of a bit) analogous to a while loop from imperative languages. We didn't even need dedicated loop syntax for it! (which is the real reason Haskell has no such syntax; if you need this kind of thing you can express it as a function). It's not the only way to do so, and experienced Haskell programmers would probably use other standard library functions to do this kind of job, rather than writing while.
But I think it's useful to see how you can express this kind of thing in Haskell. It does show that you can't translate things like imperative loops directly into Haskell; I didn't end up translating your loop in terms of my while because it ends up pretty pointless; you never use the result of function1 or function2, they're called with no arguments so they'd always return the same thing in every iteration, and function3 likewise always returns the same thing, and can only return true or false to either cause while to keep looping or stop, with no information resulting.
Presumably in the C++ program they're all using side effects to actually get some work done. If they operate on in-memory things then you need to translate a bigger chunk of your program at once to Haskell for the translation of this loop to make any sense. If those functions are doing IO then you'll need to do this in the IO monad in Haskell, for which my while function doesn't work, but you can do something similar.
[1] As an aside, it's worth trying to understand that "you can't change variables" in Haskell isn't just an arbitrary restriction, nor is it just an acceptable trade off for the benefits of purity, it is a concept that doesn't make sense the way Haskell wants you to think about Haskell code. You're writing down expressions that result from evaluating functions on certain arguments: in f x = x + 1 you're saying that f x is x + 1. If you really think of it that way rather than thinking "f takes x, then adds one to it, then returns the result" then the concept of "having side effects" doesn't even apply; how could something existing and being equal to something else somehow change a variable, or have some other side effect?
You should write a solution to your problem in a more functional approach.
However, some code in haskell works a lot like imperative looping, take for example state monads, terminal recursivity, until, foldr, etc.
A simple example is the factorial. In C, you would write a loop where in haskell you can for example write fact n = foldr (*) 1 [2..n].
If you've two functions f :: a -> b and g :: b -> c where a, b, and c are types like String or [Int] then you can compose them simply by writing f . b.
If you wish them to loop over a list or vector you could write map (f . g) or V.map (f . g), assuming you've done Import qualified Data.Vector as V.
Example : I wish to print a list of markdown headings like ## <number>. <heading> ## but I need roman numerals numbered from 1 and my list headings has type type [(String,Double)] where the Double is irrelevant.
Import Data.List
Import Text.Numeral.Roman
let fun = zipWith (\a b -> a ++ ". " ++ b ++ "##\n") (map toRoman [1..]) . map fst
fun [("Foo",3.5),("Bar",7.1)]
What the hell does this do?
toRoman turns a number into a string containing the roman numeral. map toRoman does this to every element of a loop. map toRoman [1..] does it to every element of the lazy infinite list [1,2,3,4,..], yielding a lazy infinite list of roman numeral strings
fst :: (a,b) -> a simply extracts the first element of a tuple. map fst throws away our silly Meow information along the entire list.
\a b -> "##" ++ show a ++ ". " ++ b ++ "##" is a lambda expression that takes two strings and concatenates them together within the desired formatting strings.
zipWith :: (a -> b -> c) -> [a] -> [b] -> [c] takes a two argument function like our lambda expression and feeds it pairs of elements from it's own second and third arguments.
You'll observe that zip, zipWith, etc. only read as much of the lazy infinite list of Roman numerals as needed for the list of headings, meaning I've number my headings without maintaining any counter variable.
Finally, I have declared fun without naming it's argument because the compiler can figure it out from the fact that map fst requires one argument. You'll notice that put a . before my second map too. I could've written (map fst h) or $ map fst h instead if I'd written fun h = ..., but leaving the argument off fun meant I needed to compose it with zipWith after applying zipWith to two arguments of the three arguments zipWith wants.
I'd hope the compiler combines the zipWith and maps into one single loop via inlining.
I'm new to Haskell. I know I can create a reverse function by doing this:
reverse :: [a] -> [a]
reverse [] = []
reverse (x:xs) = (Main.reverse xs) ++ [x]
Is there such a thing as (xs:x) (a list concatenated with an element, i.e. x is the last element in the list) so that I put the last list element at the front of the list?
rotate :: [a] -> [a]
rotate [] = []
rotate (xs:x) = [x] ++ xs
I get these errors when I try to compile a program containing this function:
Occurs check: cannot construct the infinite type: a = [a]
When generalising the type(s) for `rotate'
I'm also new to Haskell, so my answer is not authoritative. Anyway, I would do it using last and init:
Prelude> last [1..10] : init [1..10]
[10,1,2,3,4,5,6,7,8,9]
or
Prelude> [ last [1..10] ] ++ init [1..10]
[10,1,2,3,4,5,6,7,8,9]
The short answer is: this is not possible with pattern matching, you have to use a function.
The long answer is: it's not in standard Haskell, but it is if you are willing to use an extension called View Patterns, and also if you have no problem with your pattern matching eventually taking longer than constant time.
The reason is that pattern matching is based on how the structure is constructed in the first place. A list is an abstract type, which have the following structure:
data List a = Empty | Cons a (List a)
deriving (Show) -- this is just so you can print the List
When you declare a type like that you generate three objects: a type constructor List, and two data constructors: Empty and Cons. The type constructor takes types and turns them into other types, i.e., List takes a type a and creates another type List a. The data constructor works like a function that returns something of type List a. In this case you have:
Empty :: List a
representing an empty list and
Cons :: a -> List a -> List a
which takes a value of type a and a list and appends the value to the head of the list, returning another list. So you can build your lists like this:
empty = Empty -- similar to []
list1 = Cons 1 Empty -- similar to 1:[] = [1]
list2 = Cons 2 list1 -- similar to 2:(1:[]) = 2:[1] = [2,1]
This is more or less how lists work, but in the place of Empty you have [] and in the place of Cons you have (:). When you type something like [1,2,3] this is just syntactic sugar for 1:2:3:[] or Cons 1 (Cons 2 (Cons 3 Empty)).
When you do pattern matching, you are "de-constructing" the type. Having knowledge of how the type is structured allows you to uniquely disassemble it. Consider the function:
head :: List a -> a
head (Empty) = error " the empty list have no head"
head (Cons x xs) = x
What happens on the type matching is that the data constructor is matched to some structure you give. If it matches Empty, than you have an empty list. If if matches Const x xs then x must have type a and must be the head of the list and xs must have type List a and be the tail of the list, cause that's the type of the data constructor:
Cons :: a -> List a -> List a
If Cons x xs is of type List a than x must be a and xs must be List a. The same is true for (x:xs). If you look to the type of (:) in GHCi:
> :t (:)
(:) :: a -> [a] -> [a]
So, if (x:xs) is of type [a], x must be a and xs must be [a] . The error message you get when you try to do (xs:x) and then treat xs like a list, is exactly because of this. By your use of (:) the compiler infers that xs have type a, and by your use of
++, it infers that xs must be [a]. Then it freaks out cause there's no finite type a for which a = [a] - this is what he's trying to tell you with that error message.
If you need to disassemble the structure in other ways that don't match the way the data constructor builds the structure, than you have to write your own function. There are two functions in the standard library that do what you want: last returns the last element of a list, and init returns all-but-the-last elements of the list.
But note that pattern matching happens in constant time. To find out the head and the tail of a list, it doesn't matter how long the list is, you just have to look to the outermost data constructor. Finding the last element is O(N): you have to dig until you find the innermost Cons or the innermost (:), and this requires you to "peel" the structure N times, where N is the size of the list.
If you frequently have to look for the last element in long lists, you might consider if using a list is a good idea after all. You can go after Data.Sequence (constant time access to first and last elements), Data.Map (log(N) time access to any element if you know its key), Data.Array (constant time access to an element if you know its index), Data.Vector or other data structures that match your needs better than lists.
Ok. That was the short answer (:P). The long one you'll have to lookup a bit by yourself, but here's an intro.
You can have this working with a syntax very close to pattern matching by using view patterns. View Patterns are an extension that you can use by having this as the first line of your code:
{-# Language ViewPatterns #-}
The instructions of how to use it are here: http://hackage.haskell.org/trac/ghc/wiki/ViewPatterns
With view patterns you could do something like:
view :: [a] -> (a, [a])
view xs = (last xs, init xs)
someFunction :: [a] -> ...
someFunction (view -> (x,xs)) = ...
than x and xs will be bound to the last and the init of the list you provide to someFunction. Syntactically it feels like pattern matching, but it is really just applying last and init to the given list.
If you're willing to use something different from plain lists, you could have a look at the Seq type in the containers package, as documented here. This has O(1) cons (element at the front) and snoc (element at the back), and allows pattern matching the element from the front and the back, through use of Views.
"Is there such a thing as (xs:x) (a list concatenated with an element, i.e. x is the last element in the list) so that I put the last list element at the front of the list?"
No, not in the sense that you mean. These "patterns" on the left-hand side of a function definition are a reflection of how a data structure is defined by the programmer and stored in memory. Haskell's built-in list implementation is a singly-linked list, ordered from the beginning - so the pattern available for function definitions reflects exactly that, exposing the very first element plus the rest of the list (or alternatively, the empty list).
For a list constructed in this way, the last element is not immediately available as one of the stored components of the list's top-most node. So instead of that value being present in pattern on the left-hand side of the function definition, it's calculated by the function body onthe right-hand side.
Of course, you can define new data structures, so if you want a new list that makes the last element available through pattern-matching, you could build that. But there's be some cost: Maybe you'd just be storing the list backwards, so that it's now the first element which is not available by pattern matching, and requires computation. Maybe you're storing both the first and last value in the structures, which would require additional storage space and bookkeeping.
It's perfectly reasonable to think about multiple implementations of a single data structure concept - to look forward a little bit, this is one use of Haskell's class/instance definitions.
Reversing as you suggested might be much less efficient. Last is not O(1) operation, but is O(N) and that mean that rotating as you suggested becomes O(N^2) alghorhim.
Source:
http://www.haskell.org/ghc/docs/6.12.2/html/libraries/base-4.2.0.1/src/GHC-List.html#last
Your first version has O(n) complexity. Well it is not, becuase ++ is also O(N) operation
you should do this like
rotate l = rev l []
where
rev [] a = a
rev (x:xs) a = rev xs (x:a)
source : http://www.haskell.org/ghc/docs/6.12.2/html/libraries/base-4.2.0.1/src/GHC-List.html#reverse
In your latter example, x is in fact a list. [x] becomes a list of lists, e.g. [[1,2], [3,4]].
(++) wants a list of the same type on both sides. When you are using it, you're doing [[a]] ++ [a] which is why the compiler is complaining. According to your code a would be the same type as [a], which is impossible.
In (x:xs), x is the first item of the list (the head) and xs is everything but the head, i.e., the tail. The names are irrelevant here, you might as well call them (head:tail).
If you really want to take the last item of the input list and put that in the front of the result list, you could do something like:
rotate :: [a] -> [a]
rotate [] = []
rotate lst = (last lst):(rotate $ init lst)
N.B. I haven't tested this code at all as I don't have a Haskell environment available at the moment.