Loops in Functional Languages
By: James Carlson
Tags: Functional Programming, Elm Lang, Elm

Adventures in Programming

In this article, we are going to look at how loops are used, first in an imperative language, like Python, then in a pure functional language, like Elm. While the particular languages used are not as important as the ideas, these are languages that I have used a lot, and which I like a lot, though for different purposes. The first few examples will be mathematical, but involve nothing more exotic that adding numbers. The very last example is about text processing.

The article is organized as follows:

Imperative Loops recalls how we do loops in an imperative language like Python, with a side note on the notion of infinity in the fourteenth century.

recalls how we do loops in an imperative language like Python, with a side note on the notion of infinity in the fourteenth century. A Primer on Functional Languages reviews some basic notions, that functions have types a -> b or a -> b -> c . Skip if you know this already

reviews some basic notions, that functions have types or . Skip if you know this already Reduce and fold presents a standard way of doing loops in functional languages. Can be skipped if you know about reduce and fold .

presents a standard way of doing loops in functional languages. Can be skipped if you know about and . Functional Loops is the fun part. We will talk about the type Step state a and all the goodness that flows from it.

1 Imperative Loops

Let’s go back to the days when we first learned to program, computing things like the sum 1 + 2 + 3 + … + 9. This is a task perfectly suited to the for loop , as in the Python example below.

>>> sum = 0

>>> for i in range(1,10):

... sum = sum + i

>>> sum

45

For loops are good when we know the number of times the loop body will be executed. However, that is not always the case. Consider the so-called harmonic sum,

h(n) = 1 + 1/2 + 1/3 + ... + 1/n

It was known to the mathematician|bishop Nicole Oresme (ca. 1323–1382) that this sum diverges, so that if you add all the terms, you get infinity. Since neither you nor a computer can do infinitely many additions, what this really means is the following. Choose a number L. Then there is always an n such that h(n) > L. We can write a program to find n given L:

>>> sum = 0

>>> n = 0

>>> L = 10

>>> while sum < L:

... n = n + 1

... sum = sum + 1.0/n

...

>>> n

12367

2 A Primer on Functional languages

Let’s do a quick review of some notions common in typed functional languages like Haskell and Elm. (Skip if you already know this.) Consider first this little fragment.

add : Int -> Int -> Int

add x y = x + y

It defines a function which adds two numbers:

> add 2 3

5

But what does the funny looking Int -> Int -> Int mean? Two points to explain this. First, every piece of data has a type. Thus 2 has type Int and “hello” has type String . Second, functions are data, so they must also have a type. Consider this snippet:

> String.length "hello"

5

The function String.length takes a String as input and produces an Int as output. Thefore it has type String -> Int . Upping our game, the function add takes two Int’s as input and produces an Int as output, so it has type Int -> Int -> Int . This notation suggests something. What is the meaning of the expression add 1 ? Well, it is a function of type Int -> Int , namely the function that adds 1 to its input. By applying add to the integer one, we obtain a function of type Int -> Int from a function of type Int -> Int -> Int . This sort of things, called “currying” by some and “partial application” by others, is quite useful, as we see next.

Mapping a Function over a List

Consider the task of incrementing all the integers in a list. We can do it this way:

> List.map (add 1) [1, 3, 5, 7]

[2,4,6,8]

We used List.map to apply the function add 1 to each element of the list [1, 3, 5 7] , obtaining the new list [2, 4, 6, 8] . Quiz time! Since List.map is a function, it must have a type. What is it? Well, it is something more exotic than we have seen so far:

(a -> b) -> List a -> List b

This means that List.map takes two arguments. The first argument is a function of type a -> b . This function (let’s call it f ) takes things of type a and returns things of type b . The second argument is a list of things of type a . Thus List.map applies f to every element a list of things of type a to produce a list of things of type b .

3 Reduce and Fold

One way to do loop computations in a language of pure functions is to use reduce or a fold. We will use List.foldl (“fold left”) in Elm. This is a function which takes three arguments:

a reducer, which is a function type a -> b -> b

an initial value of type b

a list of things of type a

The result is a thing of type b . As an example, we find the total number of characters in a list of strings. First, make the definition

> reducer str acc = acc + String.length str

Thus reducer "hello" 2 has the value 5 + 2 = 7. Now do this:

> List.foldl reducer 0 ["the", "little", "frog", "jumped", "high"]

23

The general form is List.foldl reducer accumulator inputList . List.foldl operates by repeatedly “eating” elements of inputList , using reduce to update accumulator each time. When the list is empty, it returns the accumulator. Done!

The sum 1 + 2 + … + n

Let’s redo some of the computations of the first section using pure functions. To add the numbers 1, 2, … 9, we do this:

> List.foldl (\i acc -> i + acc) 0 (List.range 1 9)

45

Here \i acc -> i + acc is the “anonymous” function which adds its arguments i and acc . Naturally enough, List.range is used to create lists of integers:

> List.range 1 9

[1,2,3,4,5,6,7,8,9]

Harmonic sums

Let’s compute the harmonic sum

h(n) = 1 + 1/2 + 1/3 + ... + 1/n

To this end, define a function that makes a list of integers [1, 2, ..., n] :

> seq n = List.range 1 n

<function> : Int -> List Int > seq 4

[1,2,3,4]

Then set

> h n = List.foldl (\i acc -> 1/(toFloat i) + acc) 0 (seq n)

<function> : Int -> Float

A brief test:

> h 1

1 : Float > h 2

1.5 : Float

Looks, good, so now we can do a real computation:

> h 1000

7.485470860550343

4 Functional Loops

The last example showed how we can compute the harmonic sum h n using List.foldl . But how do we do computations where the number of loop iterations is not known in advance? This is where a new type definition comes in:

type Step state a

= Loop state

| Done a

The state will be used to carry some, well, state through the computation, with the final value being of type a . Both state and a are type variables, which makes this a very general definition, applicable to many situations. A value of this type can be either Loop state or Done a . In the first case, Loop , there is more computation to be done using the value state . In the second case, Done , the computation is complete, and is a value of type a .

The loop function works with Step state a and a function

nextState : state -> Step state a

to drive the computation:

loop : state -> (state -> Step state a) -> a

loop s nextState =

case nextState s of

Loop s_ ->

loop s_ nextState



Done b ->

b

The function nextState : state -> Step state a computes the next state (or final value) from the current state. Let’s illustrate this with the problem of computing the sum 1 + 2 + … + n. Define a type alias ST to hold two values: a counter , which keeps track of how much computation remains to be done, and a value , in which the final result is built up, step by step:

type alias ST =

{ counter : Int, value : Int }

The next-state function is defined below:

f : ST -> Step ST Int

f st =

case st.counter of

0 ->

Done st.value _ ->

Loop { st | counter = st.counter - 1

, value = st.value + st.counter }

To see how it works, consider these two examples:

> f { counter = 0, value = 4}

Done 4

and

> f {counter = 2, value = 4}

Loop { counter = 1, value = 6 }

The effect of loop is much like that of List.fold . At each stage of the computation, the counter is added to the current value and then the counter is decreased by 1. This continues until the counter reaches 0, at which point value is returned. Note that the loop construct requires some care. What would happen if we said counter = st.counter + 1 ?

Harmonic sums again

The same construct applies with almost no change to compute harmonic sums. First, a slight redefinition

type alias ST2 =

{ counter : Int, value : Float }

Then a new next-state function:

f2 : ST2 -> Step ST2 Float

f2 st =

case st.counter of

0 ->

Done st.value _ ->

Loop { st | counter = st.counter - 1

, value = st.value + 1 / toFloat st.counter }

Finally, the computation:

> h n = loop { counter = n, value = 0} f2

<function> : Int -> Float > h 2

1.5 : Float > h 100

5.1873775176396215

Functional While Loops

At long last we give the functional version of the computation

>>> sum = 0

>>> n = 0

>>> L = 10

>>> while sum < L:

... n = n + 1

... sum = sum + 1.0/n

...

>>> n

12367

To begin, define a type alias ST3 which holds the necessary information: the limit value L, the current value of n, the current sum:

type alias ST3 =

{ limit : Float, n : Int, sum : Float }

Then define the next-state function that either terminates the computation or advances it:

f3 : ST3 -> Step ST3 Int

f3 st =

if st.value >= st.limit then

Done st.index else

Loop { st | index = st.index + 1

, value = st.value + 1 / toFloat (st.index + 1) }

Finally use loop to run the next-state function on some initial data:

> loop { limit = 10.0, n = 0, sum = 0} f3

12367

We can roll this work into the following definition:

numberOfTerms : Float -> Int

numberOfTerms l = loop { limit = l, n = 0, sum = 0} f3

Now comes the interesting question: is numberOfTerms really a function? That is, given a number l: Float , will int always return an Int ? The answer cannot be found by inspecting the code. Rather, one needs another piece of information, Oresme’s result that the harmonic series diverges. Given this, one can say that numberOfTerms l will return a result given a enough time. The catch, however, is that the time may be longer than the lifetime of the computer on which the program runs. This make the loop construct fundamentally different from fold and reduce , which are guaranteed to terminate.

Text Processing

We end with an optional, somewhat long example of text processing. Imagine an array of strings (“lines”) in which lines have no newline character and in which paragraphs are separated by empty strings. The goal is to find the paragraph boundaries corresponding to a line with given array index. To this end, set

type ParagraphBoundary

= BeginParagraph

| EndParagraph

The function we seek looks like this:

> boundary : ParagraphBoundary -> Int -> Array String -> Int

boundary boundary lineNumber lines = ...

Thus the function function call boundary BeginParagraph 5 lines will find the beginning of the paragraph containing the line with array index 5. For example, if

someLines = ["a", "b", "", "c", "d", "e", "f", "", "g", "h"]

the boundary BeginParagraph 5 someLines = 3 , while boundary EndParagraph 5 = 6 . We present the implementation of the boundary function below with little comment. They main point is that by using the loop construct, one achieves an efficient implementation that generally requires scanning only a small part of the array.

Implementation

Since we need to be able to search both forwards and backwards, we make this definition:

type Direction

= Forward

| Backward

The state is defined by

type alias ST4 =

{ lastIndex : Int, lines : Array String, currentLine : Int }

with boundary defined like this:

boundary : Boundary -> Position -> Array String -> Int

boundary boundary position lines =

let

initialState =

{ lastIndex = Array.length lines - 1

, lines = lines

, currentLine = position.line }

in

case boundary of

BeginParagraph ->

loop initialState (next Backward)



EndParagraph ->

loop initialState (next Forward)

Finally, here is the next-state function: