A blog exploring functional programming and Swift.

Monday May 7, 2018

In episode #14 we explored the idea of contravariance and it led us to some interesting forms of compositions. The episode also had a large number of exercises, 18 in total(!), and they went pretty deep into topics that we didn’t have time to cover.

In today’s Point-Free Pointer we want to provide solutions to those exercises. If you haven’t yet had a chance to try solving them on your own, we highly recommend giving it a shot before reading further.

Determine the sign of all the type parameters in the function (A) -> (B) -> C. Note that this is a curried function. It may be helpful to fully parenthesize the expression before determining variance.

Recall from the episode on exponents and algebraic data types
that function arrows parenthesize to the right, which means when we write `(A) -> (B) -> C`

, we really
mean `(A) -> ((B) -> C)`

. Now we can apply the bracketing method we demonstrated in the episode:

```
// (A) -> ((B) -> C)
// |_| |_|
// -1 +1
// |_| |________|
// -1 +1
```

So now we see that `A`

is negative, `B`

is also negative, and `C`

is positive.

Determine the sign of all the type parameters in the following function:

`(A, B) -> (((C) -> (D) -> E) -> F) -> G`

We will apply the bracketing method to this expression, it’s just a little more involved:

```
// (A, B) -> (((C) -> (D) -> E) -> F) -> G
// |_| |_| |_|
// -1 -1 +1
// |_||_| |_______________| |_|
// +1 +1 -1 +1
// |____| |______________________| |_|
// -1 -1 +1
```

That’s intense! One tricky part is how we determined that the `A`

and `B`

inside the tuple were in
positive position, and this is because tuples are naturally a covariant structure: you can define
`map`

on the first and second components.

Now all we have to do is trace through the layers and multiply all the signs:

`A = -1 * +1 = -1`

`B = -1 * +1 = -1`

`C = -1 * -1 * -1 = -1`

`D = -1 * -1 * -1 = -1`

`E = -1 * -1 * +1 = +1`

`F = -1 * +1 = -1`

`G = +1`

And there you have it!

Recall that a setter is just a function

`((A) -> B) -> (S) -> T`

. Determine the variance of each type parameter, and define a map and contramap for each one. Further, for each map and contramap write a description of what those operations mean intuitively in terms of setters.

Again applying the bracketing method we see:

```
// ((A) -> B) -> (S) -> T
// |_| |_|
// -1 +1
// |________| |_| |_|
// -1 -1 +1
```

So now we see:

`A = -1 * -1 = +1`

`B = -1 * +1 = -1`

`S = -1`

`T = +1`

This means we should be able to define `map`

on `A`

and `T`

, and `contramap`

on `B`

and `S`

. Here
are the implementations of each, with comments that show the types of all the parts we need to plug
together:

```
typealias Setter<S, T, A, B> = ((@escaping (A) -> B) -> (S) -> T
func map<S, T, A, B, C>(_ f: @escaping (A) -> C)
-> (@escaping Setter<S, T, A, B>)
-> Setter<S, T, C, B> {
return { setter in
return { update in
return { s in
// f: (A) -> C
// setter: ((A) -> B) -> (S) -> T
// update: (C) -> B
// s: S
setter(f >>> update)(s)
}
}
}
}
func map<S, T, U, A, B>(_ f: @escaping (T) -> U)
-> (@escaping Setter<S, T, A, B>)
-> Setter<S, U, A, B> {
return { setter in
return { update in
return { s in
// f: (T) -> U
// setter: ((A) -> B) -> (S) -> T
// update: (A) -> B
// s: S
f(setter(update)(s))
}
}
}
}
func contramap<S, T, A, B, C>(_ f: @escaping (C) -> B)
-> (@escaping Setter<S, T, A, B>)
-> Setter<S, T, A, C> {
return { setter in
return { update in
return { s in
// f: (C) -> B
// setter: ((A) -> B) -> (S) -> T
// update: (A) -> C
// s: S
setter(update >>> f)(s)
}
}
}
}
func contramap<S, T, U, A, B>(_ f: @escaping (U) -> S)
-> (@escaping Setter<S, T, A, B>)
-> Setter<U, T, A, B> {
return { setter in
return { update in
return { u in
// f: (U) -> S
// setter: ((A) -> B) -> (S) -> T
// update: (A) -> B
// u: U
setter(update)(f(u))
}
}
}
}
```

It’s interesting to see that the implementation of `map`

on `A`

is quite similar to `contramap`

on
`B`

, and `map`

on `T`

is similar to `contramap`

on `S`

.

Define

`union`

,`intersect`

, and`invert`

on`PredicateSet`

.

These functions directly correspond to applying `||`

, `&&`

and `!`

pointwise on the predicates:

```
extension PredicateSet {
func union(_ other: PredicateSet) -> PredicateSet {
return PredicateSet { self.contains($0) || other.contains($0) }
}
func intersect(_ other: PredicateSet) -> PredicateSet {
return PredicateSet { self.contains($0) && other.contains($0) }
}
var invert: PredicateSet {
return PredicateSet { !self.contains($0) }
}
}
```

Create a predicate set

`powersOf2: PredicateSet<Int>`

that determines if a value is a power of`2`

, i.e.`2^n`

for some`n: Int`

.

There’s a fun trick you can perform to compute this easily. A power of two written in binary form
has the expression `1000...0`

, i.e. a `1`

followed by sum number of `1`

’s, whereas the number that came
just before it has the expression `111...1`

, i.e. all `1`

’s and one less digit. So, to see if an integer
`n`

is a power of two we could just `&`

the bits of `n`

and `n - 1`

and see if we get `0`

:

```
let powersOf2 = PredicateSet<Int> { n in
n > 0 && (n & (n - 1) == 0)
}
```

Use the above predicate set to derive a new one

`powersOf2Minus1: PredicateSet<Int>`

that tests if a number is of the form`2^n - 1`

for`n: Int`

.

We can `contramap`

on `powersOf2`

to shift them all by one. However, which direction do we shift?
Should we shift `-1`

or `+1`

?

Well, in order to test if a number is of the form `2^n - 1`

using our `powersOf2`

predicate set, we
first need to shift the number up one, and then check if it’s a power of two. Therefore
`powersOf2Minus1`

can be defined as such:

`let powersOf2Minus1 = powersOf2.contramap { $0 + 1 }`

Find an algorithm online for testing if an integer is prime, and turn it into a predicate

`primes: PredicateSet<Int>`

.

The easiest (and most naive) way to tell if `n`

is prime is to loop over all the integers from 2 to
`n - 1`

and see if it divides evenly into `n`

. One optimization we can make is to only loop over
integers less than or equal to `sqrt(n)`

. The reason is that `n`

’s factors cannot all be greater
than `sqrt(n)`

, for then their product would be greater than `n`

.

This description of the algorithm can be translated into code:

```
let primes = PredicateSet<Int> { n in
guard n != 2 else { return true }
let upperBound = max(2, Int(floor(sqrt(Double(n)))))
return (2...upperBound)
.lazy
.map { n % $0 == 0 }
.first(where: { $0 == true }) == nil
}
```

A few notes about this. First, we guard `n != 2`

to simplify constructing the range from `2`

to
`sqrt(n)`

. Also, the construction of the upper bound of that range is a little complicated in order
to dance around Swift’s numeric system. However, once we are passed that it’s pretty straightforward.
We use `lazy`

on the range so that we can early out the moment we find a divisor. We `map`

to compute
if a value in the range is a divisor of `n`

, and then we find the first one that is `true`

. Finally,
if that search came up `nil`

, we know we have a prime!

The intersection

`primes.intersect(powersOf2Minus1)`

consists of numbers known as Mersenne primes. Compute the first`10`

.

We made a small mistake with this one…we didn’t mean to ask for you to compute 10! That’s too many,
so let’s just compute 5. All we have to do is iterate over all positive integers and test them
against the Mersenne set. We can use `lazy`

+`filter`

+`prefix`

to do this!

```
(2...)
.lazy
.filter(mersennePrimes.contains)
.prefix(5)
.forEach { n in
print(n) // 3, 7, 31, 127, 8191
}
```

And we can see that indeed all of those primes are one less than a power of 2!

Now, this isn’t the most performant way to do this, we just wanted to illustrate the idea of combining complex sets in interesting ways. What’s a more performant way to do this?

Recall that

`&&`

and`||`

are short-circuiting in Swift. How does that translate to`union`

and`intersect`

?

Short-circuiting `&&`

and `||`

means that `false && x`

and `true || x`

does not evaluate the expression
`x`

at all since it already knows the outcome of the logical expression. So, if `x`

were an expensive
computation we would be saving all of that work.

Applying this to `PredicateSet`

, we see that since `union`

and `intersect`

were really just `||`

and
`&&`

under the hood, we can get short-circuiting behavior in our sets too. This means you should
put all of your most expensive work last when it comes to chaining `union`

’s and `intersect`

’s together.

What is the difference between

`primes.intersect(powersOf2Minus1)`

and`powersOf2Minus1.intersect(primes)`

? Which one represents a more performant predicate set?

From the last exercise we can see that `primes.intersect(powersOf2Minus1)`

is a worse performing
set than `powersOf2Minus1.intersect(primes)`

since the `primes`

calculation is much more expensive
than the `powersOf2Minus1`

computation. Try swapping the order in the `mersenne`

calculation to see
how much faster it can compute the first 5.

It turns out that dictionaries

`[K: V]`

do not have map on`K`

for all the same reasons`Set`

does not. There is an alternative way to define dictionaries in terms of functions. Do that and define map and contramap on that new structure.

A dictionary `[K: V]`

is just a “partial” mapping of keys to values, where partial means that not
every key is mapped to a value. This is precisely a function `(K) -> V?`

. Defining `map`

and
`contramap`

on this type is similar to defining it on functions, except we have to deal with that
optional value, which we can do with `map`

on optionals!

```
struct FuncDictionary<K, V> {
let valueForKey: (K) -> V?
func map<W>(_ f: @escaping (V) -> W) -> FuncDictionary<K, W> {
return .init { key in self.valueForKey(key).map(f) }
}
func contramap<L>(_ f: @escaping (L) -> K) -> FuncDictionary<L, V> {
return .init { key in self.valueForKey(f(key)) }
}
}
```

Define

`CharacterSet`

as a type alias of`PredicateSet`

, and construct some of the sets that are currently available in the API.

We can define a character set as simply a predicate set on characters:
`typealias CharacterSet = PredicateSet<Character>`

. Let’s define a character set that holds all
newlines:

```
extension PredicateSet where A == Character {
static var newlines: PredicateSet {
return PredicateSet(
contains: Set<Character>(
[
.init(Unicode.Scalar(0x000A)),
.init(Unicode.Scalar(0x000B)),
.init(Unicode.Scalar(0x000C)),
.init(Unicode.Scalar(0x000D)),
.init(Unicode.Scalar(0x0085)),
.init(Unicode.Scalar(0x2028)),
.init(Unicode.Scalar(0x2029))
]
).contains
)
}
}
```

Is

`A`

in positive or negative position in the function`(B) -> (A, A)`

? Define either`map`

or`contramap`

on`A`

.

As we have seen, tuples are a covariant construction since we can define `map`

on each of their
components (we did this with `first`

and `second`

in episodes #6
and #7. So, we’d expect functions *into* a covariant structure to
also be covariant, and indeed:

```
func map<A, B, C>(_ f: @escaping (A) -> C) -> ((B) -> (A, A)) -> ((B) -> (C, C)) {
return { g in
return { b in
let (first, second) = g(b)
return (f(first), f(second))
}
}
}
```

What we are seeing is that if a type parameter appears multiple times in a signature, and all times
it appears on the right side of `->`

, then we can consider the parameter covariant and thus can
define `map`

.

Is

`A`

in positive or negative position in`(A, A) -> B`

? Define either map or contramap.

Again, tuples are covariant, and so putting them in a negative position might turn them negative.
Let’s try defining `contramap`

:

```
func contramap<A, B, C>(_ f: @escaping (C) -> A) -> ((A, A) -> B) -> ((C, C) -> B) {
return { g in
return { c1, c2 in
let (a1, a2) = (f(c1), f(c2))
return g(a1, a2)
}
}
}
```

What we are seeing is that if a type parameter appears multiple times in a signature, and all times
it appears on the left side of `->`

, then we can consider the parameter contravariant and thus can
define `contramap`

.

Consider the type

`struct Endo<A> { let apply: (A) -> A }`

. This type is called`Endo`

because functions whose input type is the same as the output type are called “endomorphisms”. Notice that`A`

is in both positive and negative position. Does that mean that both`map`

and`contramap`

can be defined, or that neither can be defined?

Well, let’s try defining each of `map`

and `contramap`

on `Endo`

and see what goes wrong or right:

```
extension Endo {
func map<B>(_ f: @escaping (A) -> B) -> Endo<B> {
return Endo<B> { b in
b // B
f // (A) -> B
fatalError("Need to return something in B")
}
}
func contramap<B>(_ f: @escaping (B) -> A) -> Endo<B> {
return Endo<B> { b in
b // B
f // (B) -> A
fatalError("Need to return something in B")
}
}
}
```

So, the types we have at our disposal in the `Endo<B>`

block don’t exactly match up. In `map`

we need
to return something in `B`

while we have `b: B`

and `f: (A) -> B`

. Now, of course we could just return
`b`

, but we didn’t use `f`

at all, and that seems weird! Likewise, for `contramap`

we need to return
something in `B`

and have `b: B`

and `f: (B) -> A`

at our disposal, so the only thing we can do is
return `b`

and ignore `f`

entirely. Very strange!

It seems that although `map`

and `contramap`

can be defined on `Endo`

, neither one does anything
interesting. They just return the identity endomorphism, regardless of what `f`

is.

Turns out,

`Endo`

has a different structure on it known as an “invariant structure”, and it comes equipped with a different kind of function called`imap`

. Can you figure out what it’s signature should be?

Since neither `map`

nor `contramap`

were very interesting, maybe `imap`

will have more to it. In both
potential implementations of `map`

and `contramap`

on `Endo`

we saw that we were missing some way
of transporting values in the opposite direction of the `f`

provided. For example, in `contramap`

we could apply `f(b)`

to get something in `A`

, but then we would need some way to transform back to
something in `B`

.

Perhaps `imap`

needs to functions going both ways!

```
extension Endo {
func imap<B>(_ f: @escaping (A) -> B, _ g: @escaping (B) -> A) -> Endo<B> {
return Endo<B> { b in
f(self.apply(g(b)))
}
}
}
```

This shows that if a type parameter appears on *both* sides of `->`

in a function signature, then
to transform on that parameter you need something like `imap`

, which comes equipped with functions
*into and out of* the type.

Consider the type

`struct Equate<A> { let equals: (A, A) -> Bool }`

. This is just a struct wrapper around an equality check. You can think of it as a kind of “type erased”`Equatable`

protocol. Write`contramap`

for this type.

This `contramap`

is quite similar to some of the other ones we’ve written:

```
extension Equate {
func contramap<B>(_ f: @escaping (B) -> A) -> Equate<B> {
return .init { self.equals(f($0), f($1)) }
}
}
```

Consider the value

`intEquate = Equate<Int> { $0 == $1 }`

. Continuing the “type erased” analogy, this is like a “witness” to the`Equatable`

conformance of Int. Show how to use`contramap`

defined above to transform`intEquate`

into something that defines equality of strings based on their character count.

We can induce an `Equate`

value on strings by contramapping over its character count. Here are three
ways of doing it:

```
intEquate.contramap { (a: String) in a.count }
// Using the `get` function from episode #8: Key Paths and Getters
intEquate.contramap(get(\String.count))
// Using the `^` operator from episode #8: Key Paths and Getters
intEquate.contramap(^\String.count)
```

*Phew*! That was a lot!

Well, that concludes this week’s Point-Free Pointer. I hope that breaking down the exercises helped you learn something new about the material, and encouraged you to try more exercises from our episodes. They are a great opportunity to dig a lil deeper.

Until next time!

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