# 5 Types

The quote at the beginning of this chapter is an excerpt from the Wallace Stevens poem, The Idea of Order at Key West. The whole poem resonates with me; it aptly captures the aesthetics of type level programming.

## 5.3 How to read type signatures

The type constructor for function (->) isn’t magic. It’s exactly like any other type constructor. Recall how previously with lists the type constructor was []:

Prelude> :i []
data [] a = [] | a : [a]  -- Defined in ‘GHC.Types’

There isn’t any reason other than cleaner syntax that [] a or [a] couldn’t be List a.

Well the function type is almost exactly the same. There isn’t any reason why a -> b, which is (->) a b, couldn’t be Fun a b. It’s just more syntactic sugar (Haskell is a very sugary language. That’s why it’s so sweet!)

Prelude> :i (->)
data (->) t1 t2   -- Defined in ‘GHC.Prim’
infixr 0 (->)

There isn’t any definition for the function type though because it’s a primitive (hence the GHC.Prim).

### Exercises: Type Matching

1. not :: Bool -> Bool
2. length :: [a] -> Int
3. concat :: [[a]] -> [a]
4. head :: [a] -> a
5. (<) :: Ord a => a -> a -> Bool

## 5.4 Currying

All these types for f are equivalent:

type Fun = (->)
f :: a -> a -> a
f :: a -> (a -> a)
f :: (->) a ((->) a a)
f :: Fun a (Fun a a)

Functions in Haskell return one and only one thing. Partial application is kind of a silly term. What’s partial about applying a Fun a (Fun a a) to an a? You give it an a, it gives you a function. Nothing partial about that. Maybe we wanted a function.

It’s only partial if you think of functions of somehow not being final values. Which is how they are in imperative-land. But we’re not in imperative-land anymore. Here functions are first-class, so they actually are values like anything else.

In fact, if you remember any of Chapter 1, there’s a good argument to be made that functions are more real than any other value. Lambda calculus builds the whole universe out of functions. Sure literals exist, but they’re a convenience (a huge convenience) not a strict necessity.

The curry and uncurry functions in the text are useful to understand conceptually.

Sectioning is basically bad practice. It’s a great way to confuse yourself and others. Do yourself a favor and throw an abstraction on top of it:

Not

y = (2^)
z = (^2)

But rather,

y = \x -> 2^x
z = \x -> x^2

You see how much nicer that is? Don’t abuse infix operators please. They’re there to make text more legible, not more terse and inscrutable. There are definitely cases where cleverly sectioning an infix operator can make things clearer, but in my opinion, these cases are exceptions.

1. a
2. d
3. d
4. c
5. a
6. e
7. e
8. e
9. c

## 5.5 Polymorphism

Greek words are everywhere in Haskell-land. Greek and Latin, especially in the sciences, act like a kind of conceptual assembly language that allows you to build words for complex ideas out of concrete concepts. But if you don’t know the roots, the vivid metaphor embedded in the word becomes murky and unclear. It would be very confusing if we used English for this, because then the same word would be used for both substantive and metaphoric meaning.

Let’s take the word “Polymorphism” for example. This breaks apart into three Greek roots:

• “poly-”, from the Greek “πολύς”, meaning “many”.
• “-morph-”, from the Greek “μορφή”, meaning “shape, form, type”
• “-ism”, from the Greek “-ισμός”, which is a suffix forming abstract nouns.

So the word means literally “The abstract property of having many forms or types.” Or, maybe more tersely: “many-formed-ness”.

Some important Greek roots:

hyle: matter
morphe: form

polys: - many
monos: - one

autos: self
endon: in
ectos: out
isos: equal

ana: up
kata: down
epi:  upon
meta: beyond, with
para: beside
meter: measure

So when you read “parametric polymorphism”, fear not, you are really reading “beside-measure many-form-thing.” The latter doesn’t sound nearly as clever at cocktail parties, but that’s actually what the words mean, and knowing the meanings of the words you use helps you remember the concepts they describe.

A parameter is quite literally a “side measure.” When we measure a thing by looking at it next to something else, we’re using a parameter. Ever ask whether something was bigger than a breadbox? That’s measuring size in terms of breadboxes. It’s a side measure. It’s a parameter.

The function id :: a -> a is called parametrically polymorphic, because it’s using a as the side-measure, or parameter.

This is a little clearer if we enabled the ExplicitForall language extension:

{-# LANGUAGE ExplicitForAll #-}
module SideMeasure where

id' :: forall a. a -> a
id' a = a

The forall is actually implicit in the regular type definition of id, but we’ve just made it explicit here. All it’s saying is that whatever a type the argument of id' has, id' will always return something of the same type.

Interestingly this implies that the only thing we can do with id' is just return the argument unchanged. You can see this pretty clearly, I think, if you consider what has to happen if you pass in a type that only has a single value inhabiting it (like (), the Unit type).

Constrained polymorphism, by the way, is where we restrict that forall, to just a forall within some class of types (a “typeclass”, if you will). So a function like negate :: Num a => a -> a, only makes the same “for all” commitment for types within the Num typeclass. The Num a => is called a constraint, or typeclass constraint, by the way.

## Exercises: Parametricity

1. This is impossible because id has to work for a type that only one member. If a type only has one member, then the only thing a function with signature a -> a can do if passed a value of that type is return the same value (or bottom, which is in every type) without breaking the type signature.
2. f x y = x or f x y = y
3. a -> b -> b is the same as a -> (b -> b) and the only thing with type (b -> b) is the id function. So this function is a kind of constant function that takes two arguments and returns the second, as opposed to const :: a -> b -> a which takes two arguments and returns the first. One implementation would be const id, but I am unsure whether flip const counts as a separate implementation.

## 5.6 Type Inference

Type inference is a cool tool for helping us build better programs. But it works best when you give it annotations to infer from. A lot of Haskell programming involves defining the types of the top-level expressions in your program before you actually start constructing anything, so this isn’t exactly any extra work.

And if you want to see real type system magic at work: Typing the technical interview

### Exercises: Apply Yourself

1. [Char] -> [Char]
2. Fractional a => a -> a
3. Int -> [Char]
4. Int -> Bool
5. Char -> Bool

## 5.8 Chapter Exercises

1. c
2. a
3. b
4. c

### Determine the type:

1. Num a => a
2. Num a => (a, [Char])
3. (Integer, [Char])
4. Bool
5. Int
6. Bool
1. Num a => a
2. Num a => a -> a
3. Fractional a => a
4. [Char]

1. bignum $10 doesn’t make sense 5^10 is a number not a function 2. This should work. 3. c and d need a function. 4. c not in scope. ### Type variable or specific type constructor? • 0: constrained polymorphic type var • 1: fully polymorphic type var • 2: concrete • 3: concrete • 0: fully polymorphic • 1: concrete • 2: concrete • 0: fully polymorphic • 1: constrained polymorphic • 2: concrete • 0: fully polymorphic • 1: fully polymorphic • 2: concrete ### Write a type signature: 1. [a] -> a 2. (Ord a, Ord b) => a -> b -> Bool 3. (a, b) -> b ### Given a type, write the function: 1. i = id 2. c x y = x 3. yes 4. c' x y = y 5. r = tail 6. co x y z = x$ y z
7. a x y = fst(y, x y)
8. a' x y = x y

### Type-Kwon-Do

1. h x = g $f x 2. e x = w$ q x
3. xform (x, y) = (xz x, yz y)
4. munge f g x = fst $g$ f x

## 5.10 Follow-up resources

1. Luis Damas; Robin Milner. Principal type-schemes for functional programs

2. Christopher Strachey. Fundamental Concepts in Programming Languages Popular origin of the parametric/ad-hoc polymorphism distinction.