Notes (CTFP 07/31): Functors

7 Functor

functor: A functor is a mapping between categories.

f :: a -> b

F f :: F a -> F b

F :: (a -> b) -> (F a -> F b)

Compositions of functions in the base category map to the composition of the image of those functions:

h = g . f
F h = F g . F f

An object’s identity morphisms map to the identity morphisms of its image:

F (id :: A -> A)  = id :: F A -> F A

endofunctor: A functor that maps a category to itself.

7.1 Functors in Programming

7.1.1 The Maybe Functor

data Maybe a = Nothing | Just a

fmap lifts a function onto the functorial target category. For Maybe:

fmap :: (a -> b) -> (Maybe a -> Maybe b)

In general:

fmap :: Functor f => (a -> b) -> (f a -> f b)

The functor maps both objects and categories, but the objects are types not values, so we need

F :: * -> *

which is Maybe. See this Stack Overflow question for more detail.

7.1.2 Equational Reasoning

Equational reasoning is possible with pure functions, because the equality is not hiding state mutation. Thus, reasoning about code in functional programming language like Haskell becomes more explicitly mathematical.

7.1.3 Optional

Skipping C++ content. Perhaps I will come back and do this with Rust

7.1.4 Typeclasses

Suppose Maybe is a Functor. Maybe is a type constructor. This means Maybe itself is the mapping from objects to objects (* -> *) e.g. Int -> Maybe Int.

But to be a functor, Maybe needs a mapping from morphisms to morphisms. This is fmap, which is declared in Haskell in the Functor class:

class Functor f where
  fmap :: (a -> b) -> f a -> f b

To say that Maybe is an instance of this class, we have to give the implementation of fmap:

instance Functor Maybe where
  fmap f Nothing = Nothing
  fmap f (Just a) = Just (f a)

7.1.5 Functor in C++

Skipping C++.

7.1.6 The List Functor

data List a = Nil | Cons a (List a)

An fmap for List has to have the type:

fmap :: (a -> b) -> (List a -> List b)

If we have a List of a’s and a function that transforms a’s into b’s, what’s the most natural way to get a List of b’s? To apply the transformation to each a in the list!

instance Functor List where
  fmap _ Nil = Nil
  fmap f (Cons x t) = Cons (f x) (fmap f t)

7.1.7 The Reader Functor

In Haskell, a function type is constructed using the arrow type constructor:

(->) a b

Because (->) is a type constructor that takes two arguments, (->) a is a type constructor, like List or Maybe.

Let’s give wrap type constructor (->) in a newtype called Reader:

{-# LANGUAGE InstanceSigs #-}
newtype Reader r a = Reader { run :: r -> a }

instance Functor (Reader r) where
  fmap :: (a -> b) -> (Reader r a) -> (Reader r b)
  fmap f reader = Reader $ f . (run reader)

We could also say:

fmap f (Reader g) = Reader $ f . g

7.2 Functors as Containers

The idea of a Functor as a container of some value or values is imperfect, since sometimes a Functor will explicitly contain no values, as in the case of the Const functor:

{-# LANGUAGE InstanceSigs #-}
data Const c a = Const c

instance Functor (Const c) where
  fmap :: (a -> b) -> Const c a -> Const c b
  fmap _ (Const v) = (Const v)

A Const c a is a box containing a type c and a “phantom” type a. The type a doesn’t exist except in the type signature.

7.3 Functor Composition

Lets say we have a list of integers:

ints :: [Int]
ints = [0,1,2,3,4,5,6,7,8,9]

Now suppose we want to apply the function:

isPrime :: Int -> Bool

to this list. The specific implementation of isPrime isn’t important, it just returns True if the Int is prime and False if it is not prime.

But we have a problem here, in that prime numbers are only defined for integers greater than 1. isPrime 3 should return True and isPrime 4 should return False, but isPrime 1 is undefined and should return neither of those things.

Now, you might be saying, why don’t we just return False for any integer where isPrime is undefined? This does seem like the simplest solution from a programming perspective.

However, this solution could actually generate a lot of headache for us later on.

For example, let’s say we wanted to write another function isComposite, that returns True for integers with factors.

Mathematical intuition says that we should be able to do:

isComposite :: Int -> Bool
isComposite = not . isPrime

But if we defined isPrime 1 to be False then isComposite 1 would return True, which is wrong, because “composite” numbers are also only defined for numbers greater than 1.

Now, suppose we have some code (maybe from a library) where isPrime and isComposite do follow the rule that each is the logical inverse (the “not) of the other.

But let’s say that for whatever reason, the author decided to make isPrime and isComposite partial functions, meaning that for any integers outside their domain (less than or equal to 1) they return a runtime error.

Runtime errors, are annoying (and dangerous) so, let’s say we want to fix their code.

We could directly modify their code and write two new functions:

safeIsPrime :: Int -> Maybe Bool
safeIsComposite :: Int -> Maybe Bool

So that isPrime 1 return Nothing and isComposite 4 returns Just True.

Editing other peoples’ code is a lot of work though, and isn’t always the best solution. Is there a way we can turn make partial functions safe from the “outside”?

There is! We know that

safen :: Int -> Maybe Int
safen x
  | x > 1 = Just x
  | otherwise = Nothing

safeIsPrime x= safen