# Unfold

## Optional background information

A fellow recurser named Emily Xie held a workshop to teach p5.js, which she used to make a few beautiful sketches. At the end, she challenged us to create a rain of green characters in the style of the Matrix, as she had. I accepted her challenge, but got bored of reading tutorials and docs, so I decided to start learning my way around the library by rewriting Emily’s code to be mostly functional.

You can see my sketch here and my code for it here if you’re interested, but this blog post isn’t about p5, although p5 is super cool and well worth writing about. I’ll try to find time to do that after I’ve figured out how I want to post my second rewritten sketch online and created a few original sketches.

This blog post is about the `unfold` function, which I happened to use in this context, but which is also useful in many other contexts. After using it, I thought to myself: “I feel like many people are familiar with `fold` (or `reduce`), but not many people are familiar with `unfold`. Why don’t I tell them?”

So I did at the Recurse Center’s Thursday presentations on June 9th. This blog post is an extended version of that 5 minute talk.

I give examples in both Haskell and Javascript so hopefully many readers can understand! The concept of an `unfold` is not specific to any particular programming language.

## What is unfold?

As you might guess if you’re already familiar with `fold` or `reduce` in any of its many guises, `unfold` is the opposite (or more precisely, the dual) of `fold`. Like `fold`, `unfold` is a higher order function (i.e. a function that takes a function as an argument), which are typically nicer to use in functional programming than direct recursion.

Recursive equations are the “assembly language” of functional programming, and direct recursion the goto.
– Jeremy Gibson, “Origami Programming”

In a `fold`, we consume a recursive data structure one piece at a time to produce some sort of summary value.

In an `unfold`, we generate a recursive data structure one piece at a time making use of some sort of state.

The function we pass to `unfold` as an argument should somehow be able to distinguish between the following two cases:

1. There is both a value to be added to the data structure, and an updated state.
2. We are done building the data structure.

The `unfold` function can be defined in many ways, in many languages, for many types of data structures. How we define it has relevance for the “shape” or type signature of the functions we pass to it. But I think you’ll feel a kind of basic similarity no matter how we do it.

Here’s a stateful implementation of `unfold` for arrays in old-school, iterative or procedural or C-style Javascript, relying on the convention that the function `f` should return an array containing the value to be added to the output array as the zeroth element, and an updated state as the first element, until the function is done building the array, when it should return `false` (or some falsy value).

(I borrowed this convention from ramda.js, which is the library I imported in my actual program.)

``````  function unfold(f, seed) {
var arr = [];
var state = seed;

var next;
while (next = f(state)) {
state = next[1];
arr.push(next[0]);
}

return arr;
}
``````

We can use it to write a function which makes an array of all the even numbers less than some bound passed as an argument. We can even be fancy and use the new Javascript standard, ES2015, to make it a one-liner:

``````const evensUpTo = n => unfold(current => current >= n ? false : [current, current+2], 0)
}
``````

If we call `evensUpTo(10)` we get the expected `[0, 2, 4, 6, 8]`.

If you write a function (or procedure, or whatever) that mutates internal state to present a pure interface, are you doing functional programming?

If a tree falls in the forest and no one hears, does it make it a sound?

I try not to spend my time pondering such questions, although I often can’t help myself. Anyway, if it pleases you more, here’s a purely functional implementation of `unfold` for a linked list in Haskell:

``````unfold :: (s -> Maybe (a, s)) -> s -> [a]
unfold f state = case f state of
Just (x, newState) -> x : unfold f newState
Nothing            -> []
``````

The type signature gives us a lot of information about `unfold`. For one thing, the type `s` of the state is generic: we can use this for any type of state. Same for the type `a` of elements in the output list – we can use this to build any type of output list: `[Int]`, `[String]`, etc.

Here, `f` is a function which operates on some input state `s`, and returns an optional tuple of a value to put in the list and an updated state. In the case that the function returns `Nothing` (represented explicitly as one of the two constructors of Haskell’s parameterized `Maybe` type, since Haskell has no `null` or `undefined`), we reach the terminating case of the empty list, and so we are done.

I’ll tell you, this pleases me more. I consider this code far more clear than my Javascript implementation of `unfold` above, but your opinion may vary.

We can do a lot of fun things with `unfold`. If you didn’t already know, Haskell’s laziness means we can define infinite lists with no problem, as long as we don’t attempt to summon too much of the list into memory. (To avoid such problems we can use the `take` function, which takes two arguments: how many elements you want to take from a list, and a list which might be very long. Haskell will evaluate no more elements of the list than we actually ask to examine.)

So here’s a list of all the Fibonnaci numbers:

``````fibs :: [Integer]
fibs = unfold (\(f0, f1) -> Just (f1, (f1, f0+f1))) (0, 1)
``````

We always have the current and previous fibonacci numbers as state at each iteration; we continually add the current (larger) number to our output list of all the fibonacci numbers. To update the state: the sum of the two numbers becomes the new larger number, i.e. the next iteration’s fibonacci number, and the previously larger number becomes the new smaller number, i.e. the next iteration’s previous fibonnaci number.

In other words, this is a correct hand-optimization of the naive recursive implementation:

``````naiveFibs :: [Integer]
naiveFibs = map fib [0..]
where
fib 0 = 1
fib 1 = 1
fib n = fib (n-1) + fib (n-2)
``````

(`[0..]` is Haskell shorthand for the infinite list of all natural numbers, starting from zero and counting up by one.)

If we `take 10 fibs` we obtain the expected `[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]`.

## Conclusion

The next time you write a for loop to build a data structure, consider an `unfold`! You can do it for much more complex data structures than the Javascript array and Haskell linked list I chose for my examples here.

If you can share code you refactored (or thought to write with an `unfold` in the first place), tell me about it, I’d be interested to read it!

## References

I used this presentation by Conal Elliott in my presentation of `unfold` at Recurse Center. I also lifted the Jeremy Gibson quote above from it. It might be hard going if you don’t already know Haskell or a similar language. But it covers far more material than this introductory blog post.

For those who can read a little Haskell but have a hard time understanding the nonstandard syntax in that presentation, this blog post gives a good intuition for it.

Hopefully, those without a decent reading knowledge of Haskell were able to get something out of this blog post!