Custom 'fold' function needs counter - haskell

My homework went really well until I stumpled upon the last task.
First, I had to define a custom List structure:
data List a = Nil | Cons a (List a) deriving Show
Another task was to write a custom fold function:
foldList :: (a -> b -> b) -> b -> List a -> b
foldList f b Nil = b
foldList f b (Cons a l) = f a (foldList f b l)
The second parameter is the value that is used at the end of the list (at a Nil element).
I also had to write a function prodList that multiplies every element of the provided list with each other:
prodList :: List Int -> Int
prodList = foldList (\x y -> x * y) 1
The 1 at the end is the neutral element of multplication. It therefore has no effect on the calculation.
The last one, though, is hard for me to solve.
I have to write a function binList that calculates the decimal value of list that represents a binary number. The least significant bit is the first element of the list, the binary number is therefore reversed.
A given example is that the result of binList (Cons 1 (Cons 0 (Cons 0 (Cons 0 (Cons 1 Nil))))) should be 19 (since (10001)_2 is (19)_10). The result of the list [1,1,0,1], however, should be (1011)_2=(11)_10).
The culprit of the assignment is, that we have to use foldList.
I know how to calculate each digit, but I struggle to find a way to find out which index i I'm currently at:
binList :: List Int -> Int
binList = foldList (\x y -> 2^i*x + y)
There probably is a nice, curry way to solve this in Haskell. Could you explain to me how you would solve this assignment?

If you were to write out the calculation, it would look like this:
x0 + 2 * x1 + 4 * x2 + 8 * x3 + ...
This might suggest you need to use the index, but if you factorize this expression, you get this instead:
x0 + 2 * (x1 + 2 * (x2 + 2 * (x3 ...
Do you see how it can be written as a fold now? Notice that there is a self-similarity that looks kind of like this:
x + 2 * x'
Hopefully that's enough of a hint for you :)

Related

Haskell function that accepts function or value, then calls function or returns value

How can I write a type declaration and function in Haskell that takes either a function (that itself takes no arguments) or a value. When given a function it calls the function. When given a value it returns the value.
[edit] To give more context, I'm mostly curious how to solve this problem in Haskell without bit twiddling: Designing function f(f(n)) == -n
Sean
I'm mostly curious how to solve this problem in Haskell without bit twiddling: Designing function f(f(n)) == -n
That's actually quite easy to solve:
when :: (a -> Bool) -> (a -> a) -> a -> a
when p f x = if p x then f x else x
f :: Integer -> Integer
f = (+) <$> when even negate <*> signum
How do we derive this? Consider:
f (f n) = (-n) -- (0) - from the interview question
f x = y -- (1) - assumption
f y = (-x) -- (2) - from (0) and (1), f (f x) = (-x)
f (-x) = (-y) -- (3) - from (0) and (2), f (f y) = (-y)
f (-y) = x -- (4) - from (0) and (3), f (f (-x)) = x
Now, if you see the left hand sides of these equations then you'll notice that there are four cases:
f x.
f y.
f (-x).
f (-y).
Notice that the domain of the function f is divided into positive and negative numbers, x and (-x), and y and (-y). Let's assume that x and y together form the set of positive numbers and (-x) and (-y) together form the set of negative numbers.
The set of positive numbers is divided into two proper disjoint subsets, x and y. How do we divide the set of positive numbers into two proper disjoint subsets? Odd and even numbers are a good candidate. Hence, let's assume that x is the set of positive odd numbers and y is the set of positive even numbers.
Another advantage of using odd and even numbers is that when negated odd numbers remain odd and even numbers remain even. Hence, (-x) is the set of negative odd numbers and (-y) is the set of negative even numbers.
Now, consider the four cases again. Notice that the sign only changes when the number is even:
f x = y (sign does not change).
f y = (-x) (sign changes).
f (-x) = (-y) (sign does not change).
f (-y) = x (sign changes).
Hence, we only negate the number when it is even (i.e. when even negate).
Next, we need to convert odd numbers into even numbers and vice versa. The easiest way to do so is to add or subtract one from the number. However, care should be taken that the resulting number is not 0. Consider the special case of 0:
f 0 = z -- (a) - assumption
f z = (-0) -- (b) - from (0) and (a), f (f 0) = (-0)
f (-0) = (-z) -- (c) - from (0) and (b), f (f z) = (-z)
(-0) = 0 -- (d) - reflexivity
f (-0) = f 0 -- (e) - from (d)
(-z) = z -- (f) - from (a) and (c) and (e)
z = 0 -- (g) - from (d) and (f)
f 0 = 0 -- (h) - from (a) and (g)
Hence, f n = 0 if and only if n = 0. So let's consider the neighbors of 0, 1 and (-1). Both of them are odd numbers. Hence, they are not negated. However, they do need to be converted into an even number (except for 0). Hence, 1 is converted into 2 and (-1) is converted into (-2).
Thus, for odd numbers we simply add the sign of the number to the number itself.
Now, consider even numbers. We know:
f 1 = 2 -- (A)
f (-1) = (-2) -- (B)
Therefore:
f 2 = (-1) -- (C), from (0) and (A), f (f 1) = (-1)
f (-2) = 1 -- (D), from (0) and (B), f (f (-1)) = 1
We know that even numbers are always negated. Hence, 2 first becomes (-2) and vice versa. Let the original even number be n. Hence, first we negate n and then add signum n to it:
evenF n = negate n + signum n
evenF 2 = negate 2 + signum 2
= (-2) + 1
= (-1)
evenF (-2) = negate (-2) + signum (-2)
= 2 + (-1)
= 1
evenF 0 = negate 0 + signum 0
= 0 + 0
= 0
Thus, for both the odd case and the even case we add the sign of the original number to when even negate. Therefore, f is defined as:
f :: Integer -> Integer
f = (+) <$> when even negate <*> signum
Hope that helps.
You can't write a function with two different signatures (unless you use typeclasses, but typeclasses are not suitable for this problem). You must solve this in a way that lets you treat both functions and non-function values as the same type. There are two obvious options:
Use a sum type.
f :: Either (Int -> Char) Char -> Char
f (Left g) = g 1
f (Right c) = c
Use const to convert your non-function value into a function that ignores its argument:
f = ($ 42)
f chr --> '*'
f (const 'a') --> 'a'
However, since this is a very unHaskelly thing to ask for, I suspect that this is an XY problem.
You could do it like this:
data FunctionOrValue a
= Function (() -> a)
| Value a
getValue :: FunctionOrValue a -> a
getValue (Function f) = f ()
getValue (Value x) = x
However this is a bit silly.
It sounds like you're trying to defer values manually, but since Haskell is lazy, there's not normally a need to do this.
An answer based on the interview question you posted:
f n = if (abs fracN) > 1 then 1/fracN else - (1/fracN)
where
fracN = realToFrac n
The question specified that the input is an int; it did not specify that the result must also be an int.
Edit: if you must return an Int, note that the question allows you to specify a range of possible inputs. I use a limit of 1073741823 (half of the max value of a signed 32-bit int), which allows me to write this:
fint :: Int -> Int
fint 0 = 0
fint n = if (abs n) <= rangeVal then n+addend else -(n-addend)
where
rangeVal = 1073741823
negator = if n < 0 then -1 else 1
addend = negator*rangeVal
One of the nice thing in Haskell (IMHO) is there is no difference between a value and a function without parameter returning a value (thanks to lazyness AND purity). Or if you prefer, every value is in fact a function without parameter which will be evaluated when needed. Therefore there is no need to worry about that type of problem. There is no such thing like f(), it's just f.
For example, you can write
x = 3 :: Int
f = head [] :: Int -- should blow up but doesn't
head [x, f] -- note that f and x have the same type
> 3 -- doesn't blow up on f, because f is not called
head [f] -- blows up, when trying to print f

Memoization / dynamic programming in Haskell on 2 or 3 arguments

Here is a simple memoization in Haskell for function f1 taking one argument (yes, Fibonacci):
f1 = [calc n | n <- [0..]]
where calc 0 = 0
calc 1 = 1
calc n = f1 !! (n-1) + f1 !! (n-2)
Now, how would this be done for a function f2 that takes 2 arguments, or f3 that takes 3?
For f2, is the best approach a list of lists? Or can a different data structure be used?
f2 = [[calc n m | n <- [0..]] | m <- [0..]]
where calc 0 0 = 0
calc a b = // ...something using (f2 !! a !! b)
Of for f3 a b c, given that max_a * max_b * max_c is manageable, how would this memoization / dynamic programming work?
I'm looking for the simplest / most straight forward approach, using standard Haskell libs if possible.
Edit
As suggest in Chris Taylor's answer, I tried using MemoCombinators.hs v0.5.1 but it fails for me, like this:
Could not find module `Data.IntTrie'
Use -v to see a list of the files searched for.
and
Illegal symbol '.' in type
Perhaps you intended -XRankNTypes or similar flag
to enable explicit-forall syntax: forall <tvs>. <type>
I need this to run in "plain" haskell, this version: GHCi, version 7.6.3
Any tips to get it going?
I can think of two approaches -
1. MemoCombinators
The easiest way to create generic memoized functions is probably to use the data-memocombinators library. Say you have the following two argument function.
f :: Int -> Int -> Integer
f 0 _ = 1
f _ 0 = 1
f a b = f (a-1) b + f a (b-1)
You can try calling f 20 20, but be prepared to wait a while. You can easily write a memoizing version with
import Data.MemoCombinators
f :: Int -> Int -> Integer
f = memo2 integral integral f'
where
f' 0 _ = 1
f' _ 0 = 1
f' a b = f (a-1) b + f a (b-1)
Note that it's important that in the helper function f' the recursive calls are not to f' but rather to the memoized function f. Calling f 20 20 now returns almost instantly.
2. Lists of Lists of ...
If you know that the arguments to your function are Int and that you will need to use all of 0..n and 0..m to compute f (n+1) (m+1) then it might make sense to use the list of lists approach. However, note that this scales badly with the number of arguments to the function (in particular, it is difficult to tell at a glance what the function is doing if you have more than 2 arguments).
flist :: [[Integer]]
flist = [[f' n m | m <- [0..]] | n <- [0..]]
where
f' _ 0 = 1
f' 0 _ = 1
f' a b = flist !! (a-1) !! b + flist !! a !! (b-1)
f :: Int -> Int -> Integer
f a b = flist !! a !! b
Since Haskell is lazy, you can memoise a function by calling it on itself.
for example, one fibonacci generator in Haskell is this:
fibs = 0 : 1 : zipWith (+) fibs (tail fibs)
(from http://www.haskell.org/haskellwiki/The_Fibonacci_sequence)
which, uses the resulting list as its own storage for state.

Error while declaring a lambda function: declare an instance first

I am trying to understand lambda functions (i.e. anonymous functions) in Haskell by writing a few simple functions that use them.
In the following example, I am simply trying to take in 3 parameters and add two of the three using an anonymous function and adding the third parameter after that.
I am getting an error saying that I must declare an instance first.
specialAdd x y z = (\x y -> x + y) + z
I appreciate any explanation of why my example is not working and/or any explanation that would help me better understand lambda functions.
specialAdd x y z = (\x y -> x + y) + z
In this example, what you are trying to do is add a function to a number, which is not going to work. Look at (\x y -> x + y) + z: it has the form a + b. In order for such an expression to work, the a part and the b part must be numbers of the same type.
Haskell is a bit of an unusual language, so its error messages are rarely of the form "you can't do that". So what's going on here is that Haskell sees that (\x y -> x + y) is a function, and since in an expression like a + b, b must be the same type as a, it concludes that b must also be a function. Haskell also allows you to define your own rules for adding non-built-in types; so it can't just give you an error saying "you can't add two functions," but instead the error is "you have not defined a rule that allows me to add two functions."
The following would do what you want:
specialAdd x y z = ((\x y -> x + y) x y) + z
Here you are applying the function (\x y -> x + y) to arguments x and y, then adding the result to z.
A good way to practice anonymous function is to use them with high order function as fold or map.
Using map as an entry point,
Basic definition of map,
map f [] = []
map f (x:xs) = f x : f xs
Built up an example,
>>> let list = [0..4]
>>> let f x = x + 1
Applying map we obtain,
>>> map f list
[1,2,3,4,5]
Now, we can omit the declaration of f and replace it using anonymous function,
>>> map (\x->x+1) list
[1,2,3,4,5]
then we deduce, map f list == map (\x->x+1) list, thus
f = \x-> x + 1 --- The same as f x = x + 1, but this is the equivalent lambda notation.
then starting with a simple function we see how to translate it into an anonymous function and then how an anonymous function can be rely to a lambda abstraction.
As an exercise try to translate f x = 2*x.
Now more complex, an anonymous function which take two arguments,
Again an working example,
>>> let add x y = x + y
>>> foldl' add 0 [0..4]
10
Can be rewrite using anonymous function as,
>>> foldl' (\x y -> x + y) 0 [0..4]
Again using equality we deduce that add = \x y -> x + y
Moreover as in hakell all function are function of one argument, and we can partial apply it, we can rewrite our previous anonymous function as, add = \x -> (\y -> x + y).
Then where is the trick ?? Because, I just show the use of anonymous function into high order one, and starting from that, showing how this can be exploited to rewrite function using lambda notation. I mean how can it help you to learn how to write down anonymous function ?
Simply cause I've give you (show you) an existing framework using high order function.
This framework is a huge opportunity to accommodate you with this notation.
Starting from that an infinity range of exercise can be deduce, for example try to do the following.
A - Find the corresponding anonymous function ?
1 - let f (x,y) = x + y in map f [(0,1),(2,3),(-1,1)]
2 - let f x y = x * y in foldl' f 1 [1..5]
B - Rewrite all of them using lambda notation into a declarative form (f = \x-> (\y-> ...)
And so on ....
To summarize,
A function as
(F0) f x1 x2 ... xn = {BODY of f}
can always be rewrite as,
(F1) f = \x1 x2 ... xn -> {BODY of f}
where
(F2) (\x1 x2 ... xn -> {BODY of f})
F2 form are just anonymous function, a pure translation of the function into lambda calculus form. F1 is a declarative lambda notation (because we declare f, as we define it, binding it to the anonymous F2). F0 being the usual notation of Haskeller.
A last note focusing on the fact we can put parenthesis between the argument, this create a closure. Doing that mean that a subset of the function's code can be fully evaluated using a subset of the function's argument, (mean converting to a form where no more free variable occurs), but that's another story.
Here is correct form:
specialAdd a b c = ((\x y -> x + y) a b) + c
Example from Learn You a Haskell...:
zipWith (\a b -> (a * 30 + 3) / b) [5,4,3,2,1] [1,2,3,4,5]
Great explanation:
http://learnyouahaskell.com/higher-order-functions#lambdas
From what I understand Labmbda/Anonymous functions help you declare a function "inline" without the need to give it a name. The "\" (ASCII for the Greek, λ) precedes the variable names for the expression that follows the "->". For example,
(\x y -> x + y)
is an anonymous (lambda) function similar to (+). It takes two parameters of type Num and returns their sum:
Prelude> :type (+)
(+) :: Num a => a -> a -> a
Prelude> :type (\x y -> x + y)
(\x y -> x + y) :: Num a => a -> a -> a
Your example is not working because, as others have pointed out, the right hand side of it is using a lambda function, (\x y -> x + y), as a parameter for the (+) operator, which is defined by default only for parameters of type Num. Some of the beauty of the lambda function can be in its "anonymous" use. Vladimir showed how you can use the lambda function in your declaration by passing it the variables from the left side. A more "anonymous" use could be simply calling it, with variables, without giving the function a name (hence anonymous). For example,
Prelude> (\x y z -> x + y + z) 1 2 3
6
and if you like writing parentheses:
Prelude> (((+).) . (+)) 1 2 3
6
Or in a longer expression (as in your example declaration), e.g.,
Prelude> filter (\x -> length x < 3) [[1],[1,2],[1,2,3]]
[[1],[1,2]]
You are trying to use (+) as something like (Num a) => (a -> a -> a) -> a -> ?? which is not correct.
(+) is defined in the class Num and (a -> a -> a) is not an instance of this class.
What exactly are you trying to achieve ?

Definiton of length using foldr

I'm trying to understand a part in the lecture notes of a class I'm taking. It defines the length function as:
length = foldr (\_ n -> 1 + n) 0
Can someone explain me how this works? I can't wrap my mind around it.
First, type of foldr: (a -> b -> b) -> b -> [a] -> b
Taking the usage into context, foldr takes in 3 arguments: a function (that takes in a. an element of a list and b. an accumulator, and returns the accumulator), the starting value of accumulator, and a list. foldr returns the final result of the accumulator after applying the function through the list.
As for this piece of code:
length = foldr (\_ n -> 1 + n) 0
As you can see, it is missing the list - so the return value of the right hand side is a function that will take in a list and produce an Int (same type as 0). Type: [a] -> Int.
As for what the right hand side means: (\_ n -> 1 + n) 0
\ means declare an unnamed function
_ means ignore the element from the list (correspond to a in the type of foldr). As you know, foldr will go through the list and apply the function to each element. This is the element passed into the function. We don't have any use of it in a length function, so we denote that it should be ignored.
n is the parameter for the Int passed in as accumulator.
-> means return
1 + n will increment the accumulator. You can imagine that the return value is passed back to foldr and foldr saves the value to pass into the next call to the function (\_ n -> 1 + n).
The 0 outside the bracket is the starting value of the counter.
The function foldr is to fold the list with a right associative operator, you can easily understand what the function does if you use the operator(+), (The function has the same behavior as sum):
foldr (+) 0 [1,2,3,4,5] = 1+(2+(3+(4+(5+0))))
For your length function, it is equivalent to:
foldr (\_ n -> 1 + n) 0 [1,2,3,4,5] = 1+(1+(1+(1+(1+0))))
That is what the foldr for
There's several equivalent ways to understand it. First one: foldr f z [1, 2, 3, 4, ..., n] computes the following value:
f 1 (f 2 (f 3 (f 4 (f ... (f n z)))))
So in your case:
length [1,2,3,4] = foldr (\_ n -> 1 + n) 0 [1,2,3,4]
= (\_ n -> 1 + n) 1 ((\_ n -> 1 + n) 2 ((\_ n -> 1 + n) 3 ((\_ n -> 1 + n) 4 0)))
= (\_ n -> 1 + n) 1 ((\_ n -> 1 + n) 2 ((\_ n -> 1 + n) 3 (1 + 0)))
= (\_ n -> 1 + n) 1 ((\_ n -> 1 + n) 2 (1 + (1 + 0)))
= (\_ n -> 1 + n) 1 (1 + (1 + (1 + 0)))
= 1 + (1 + (1 + (1 + 0)))
= 1 + (1 + (1 + 1))
= 1 + (1 + 2)
= 1 + 3
= 4
Another one is to start from this function, which copies a list:
listCopy :: [a] -> [a]
listCopy [] = []
listCopy (x:xs) = x : listCopy xs
That may look like a trivial function, but foldr is basically just that, but except of hardcoding the empty list [] and the pair constructor : into the right hand side, we instead use some arbitrary constant and function supplied as arguments. I sometimes like to call these arguments fakeCons and fakeNil (cons and nil are the names of the : operator and [] constant in the Lisp language), because in a sense you're "copying" the list but using fake constructors:
foldr fakeCons fakeNil [] = fakeNil
foldr fakeCons fakeNil (x:xs) = fakeCons x (subfold xs)
where subfold = foldr fakeCons fakeNil
So under this interpretation, your length function is "copying" a list, except that instead of the empty list it's using 0, and instead of : it's discarding the elements and adding 1 to the running total.
And here's yet a third intepretation of foldr f z xs:
z is the solution of your problem when the list is empty.
f is a function that takes two arguments: an element of the list , and a partial solution: the solution to your problem for the list of elements that appear to the right of the element that's passed to f. f then produces a solution that's "one element bigger."
So in the case of length:
The length of an empty list is 0, so that's why you use 0 as the second argument to foldr.
If the length of xs is n, then the length of x:xs is n+1. That's what your first argument to foldr, \_ n -> n + 1, is doing: it's computing the length of a list, given as arguments the first element of the list (which in this case we ignore) and the length of the rest of the list (n).
This way of thinking about foldr is very powerful, and should not be underestimated. Basically, in the function that you pass as the first argument to foldr, you're allowed to assume that the problem you're trying to solve has already been solved for all lists shorter than the one you're dealing with. All your argument function has to do, then, is to compute an answer for a list that's one element longer.

How do I use fix, and how does it work?

I was a bit confused by the documentation for fix (although I think I understand what it's supposed to do now), so I looked at the source code. That left me more confused:
fix :: (a -> a) -> a
fix f = let x = f x in x
How exactly does this return a fixed point?
I decided to try it out at the command line:
Prelude Data.Function> fix id
...
And it hangs there. Now to be fair, this is on my old macbook which is kind of slow. However, this function can't be too computationally expensive since anything passed in to id gives that same thing back (not to mention that it's eating up no CPU time). What am I doing wrong?
You are doing nothing wrong. fix id is an infinite loop.
When we say that fix returns the least fixed point of a function, we mean that in the domain theory sense. So fix (\x -> 2*x-1) is not going to return 1, because although 1 is a fixed point of that function, it is not the least one in the domain ordering.
I can't describe the domain ordering in a mere paragraph or two, so I will refer you to the domain theory link above. It is an excellent tutorial, easy to read, and quite enlightening. I highly recommend it.
For the view from 10,000 feet, fix is a higher-order function which encodes the idea of recursion. If you have the expression:
let x = 1:x in x
Which results in the infinite list [1,1..], you could say the same thing using fix:
fix (\x -> 1:x)
(Or simply fix (1:)), which says find me a fixed point of the (1:) function, IOW a value x such that x = 1:x... just like we defined above. As you can see from the definition, fix is nothing more than this idea -- recursion encapsulated into a function.
It is a truly general concept of recursion as well -- you can write any recursive function this way, including functions that use polymorphic recursion. So for example the typical fibonacci function:
fib n = if n < 2 then n else fib (n-1) + fib (n-2)
Can be written using fix this way:
fib = fix (\f -> \n -> if n < 2 then n else f (n-1) + f (n-2))
Exercise: expand the definition of fix to show that these two definitions of fib are equivalent.
But for a full understanding, read about domain theory. It's really cool stuff.
I don't claim to understand this at all, but if this helps anyone...then yippee.
Consider the definition of fix. fix f = let x = f x in x. The mind-boggling part is that x is defined as f x. But think about it for a minute.
x = f x
Since x = f x, then we can substitute the value of x on the right hand side of that, right? So therefore...
x = f . f $ x -- or x = f (f x)
x = f . f . f $ x -- or x = f (f (f x))
x = f . f . f . f . f . f . f . f . f . f . f $ x -- etc.
So the trick is, in order to terminate, f has to generate some sort of structure, so that a later f can pattern match that structure and terminate the recursion, without actually caring about the full "value" of its parameter (?)
Unless, of course, you want to do something like create an infinite list, as luqui illustrated.
TomMD's factorial explanation is good. Fix's type signature is (a -> a) -> a. The type signature for (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) is (b -> b) -> b -> b, in other words, (b -> b) -> (b -> b). So we can say that a = (b -> b). That way, fix takes our function, which is a -> a, or really, (b -> b) -> (b -> b), and will return a result of type a, in other words, b -> b, in other words, another function!
Wait, I thought it was supposed to return a fixed point...not a function. Well it does, sort of (since functions are data). You can imagine that it gave us the definitive function for finding a factorial. We gave it a function that dind't know how to recurse (hence one of the parameters to it is a function used to recurse), and fix taught it how to recurse.
Remember how I said that f has to generate some sort of structure so that a later f can pattern match and terminate? Well that's not exactly right, I guess. TomMD illustrated how we can expand x to apply the function and step towards the base case. For his function, he used an if/then, and that is what causes termination. After repeated replacements, the in part of the whole definition of fix eventually stops being defined in terms of x and that is when it is computable and complete.
You need a way for the fixpoint to terminate. Expanding your example it's obvious it won't finish:
fix id
--> let x = id x in x
--> id x
--> id (id x)
--> id (id (id x))
--> ...
Here is a real example of me using fix (note I don't use fix often and was probably tired / not worrying about readable code when I wrote this):
(fix (\f h -> if (pred h) then f (mutate h) else h)) q
WTF, you say! Well, yes, but there are a few really useful points here. First of all, your first fix argument should usually be a function which is the 'recurse' case and the second argument is the data on which to act. Here is the same code as a named function:
getQ h
| pred h = getQ (mutate h)
| otherwise = h
If you're still confused then perhaps factorial will be an easier example:
fix (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) 5 -->* 120
Notice the evaluation:
fix (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) 3 -->
let x = (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) x in x 3 -->
let x = ... in (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) x 3 -->
let x = ... in (\d -> if d > 0 then d * (x (d-1)) else 1) 3
Oh, did you just see that? That x became a function inside our then branch.
let x = ... in if 3 > 0 then 3 * (x (3 - 1)) else 1) -->
let x = ... in 3 * x 2 -->
let x = ... in 3 * (\recurse d -> if d > 0 then d * (recurse (d-1)) else 1) x 2 -->
In the above you need to remember x = f x, hence the two arguments of x 2 at the end instead of just 2.
let x = ... in 3 * (\d -> if d > 0 then d * (x (d-1)) else 1) 2 -->
And I'll stop here!
How I understand it is, it finds a value for the function, such that it outputs the same thing you give it. The catch is, it will always choose undefined (or an infinite loop, in haskell, undefined and infinite loops are the same) or whatever has the most undefineds in it. For example, with id,
λ <*Main Data.Function>: id undefined
*** Exception: Prelude.undefined
As you can see, undefined is a fixed point, so fix will pick that. If you instead do (\x->1:x).
λ <*Main Data.Function>: undefined
*** Exception: Prelude.undefined
λ <*Main Data.Function>: (\x->1:x) undefined
[1*** Exception: Prelude.undefined
So fix can't pick undefined. To make it a bit more connected to infinite loops.
λ <*Main Data.Function>: let y=y in y
^CInterrupted.
λ <*Main Data.Function>: (\x->1:x) (let y=y in y)
[1^CInterrupted.
Again, a slight difference. So what is the fixed point? Let us try repeat 1.
λ <*Main Data.Function>: repeat 1
[1,1,1,1,1,1, and so on
λ <*Main Data.Function>: (\x->1:x) $ repeat 1
[1,1,1,1,1,1, and so on
It is the same! Since this is the only fixed point, fix must settle on it. Sorry fix, no infinite loops or undefined for you.
As others pointed out, fix somehow captures the essence of recursion. Other answers already do a great job at explaining how fix works. So let's take a look from another angle and see how fix can be derived by generalising, starting from a specific problem: we want to implement the factorial function.
Let's first define a non recursive factorial function. We will use it later to "bootstrap" our recursive implementation.
factorial n = product [1..n]
We approximate the factorial function by a sequence of functions: for each natural number n, factorial_n coincides with factorial up to and including n. Clearly factorial_n converges towards factorial for n going towards infinity.
factorial_0 n = if n == 0 then 1 else undefined
factorial_1 n = n * factorial_0(n - 1)
factorial_2 n = n * factorial_1(n - 1)
factorial_3 n = n * factorial_2(n - 1)
...
Instead of writing factorial_n out by hand for every n, we implement a factory function that creates these for us. We do this by factoring the commonalities out and abstracting over the calls to factorial_[n - 1] by making them a parameter to the factory function.
factorialMaker f n = if n == 0 then 1 else n * f(n - 1)
Using this factory, we can create the same converging sequence of functions as above. For each factorial_n we need to pass a function that calculates the factorials up to n - 1. We simply use the factorial_[n - 1] from the previous step.
factorial_0 = factorialMaker undefined
factorial_1 = factorialMaker factorial_0
factorial_2 = factorialMaker factorial_1
factorial_3 = factorialMaker factorial_2
...
If we pass our real factorial function instead, we materialize the limit of the series.
factorial_inf = factorialMaker factorial
But since that limit is the real factorial function we have factorial = factorial_inf and thus
factorial = factorialMaker factorial
Which means that factorial is a fixed-point of factorialMaker.
Finally we derive the function fix, which returns factorial given factorialMaker. We do this by abstracting over factorialMaker and make it an argument to fix. (i.e. f corresponds to factorialMaker and fix f to factorial):
fix f = f (fix f)
Now we find factorial by calculating the fixed-point of factorialMaker.
factorial = fix factorialMaker

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