doc: add typeclass.md

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Leonardo de Moura 2020-11-26 08:41:13 -08:00
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- [Functions](./functions.md)
- [Sections](./sections.md)
- [Namespaces](./namespaces.md)
- [Type classes](./typeclass.md)
- [The `do` Notation](./do.md)
- [Tactics](./tactics.md)
- [String interpolation](./stringinterp.md)

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'partial unsafe private protected ' +
'if then else ' +
'universe universes variable variables ' +
'import open theory prelude renaming hiding exposing ' +
'import open export theory prelude renaming hiding exposing ' +
'calc match with do by let extends ' +
'for in unless try catch finally mutual mut return continue break where rec ' +
'syntax macro_rules macro ' +

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# Type classes
Typeclasses were introduced as a principled way of enabling
ad-hoc polymorphism in functional programming languages. We first observe that it
would be easy to implement an ad-hoc polymorphic function (such as addition) if the
function simply took the type-specific implementation of addition as an argument
and then called that implementation on the remaining arguments. For example,
suppose we declare a structure in Lean to hold implementations of addition
```lean
# namespace Ex
structure Add (a : Type) where
add : a -> a -> a
#check @Add.add
-- Add.add : {a : Type} → Add a → a → a → a
# end Ex
```
In the above Lean code, the field `add` has type
`Add.add : {α : Type} → Add αααα`
where the curly braces around the type `a` mean that it is an implicit argument.
We could implement `double` by
```lean
# namespace Ex
# structure Add (a : Type) where
# add : a -> a -> a
def double (s : Add a) (x : a) : a :=
s.add x x
#eval double { add := Nat.add } 10
-- 20
#eval double { add := Nat.mul } 10
-- 100
#eval double { add := Int.add } 10
-- 20
# end Ex
```
Note that you can double a natural number `n` by `double { add := Nat.add } n`.
Of course, it would be highly cumbersome for users to manually pass the
implementations around in this way.
Indeed, it would defeat most of the potential benefits of ad-hoc
polymorphism.
The main idea behind typeclasses is to make arguments such as `Add a` implicit,
and to use a database of user-defined instances to synthesize the desired instances
automatically through a process known as typeclass resolution. In Lean, by changing
`structure` to `class` in the example above, the type of `Add.add` becomes
```lean
# namespace Ex
class Add (a : Type) where
add : a -> a -> a
#check @Add.add
-- Add.add : {a : Type} → [self : Add a] → a → a → a
# end Ex
```
where the square brackets indicate that the argument of type `Add a` is *instance implicit*,
i.e. that it should be synthesized using typeclass resolution. This version of
`add` is the Lean analogue of the Haskell term `add :: Add a => a -> a -> a`.
Similarly, we can register an instance by
```lean
# namespace Ex
# class Add (a : Type) where
# add : a -> a -> a
instance : Add Nat where
add := Nat.add
# end Ex
```
Then for `n : Nat` and `m : Nat`, the term `Add.add n m` triggers typeclass resolution with the goal
of `Add Nat`, and typeclass resolution will synthesize the instance above. In
general, instances may depend on other instances in complicated ways. For example,
you can declare an (anonymous) instance stating that if `a` has addition, then `Array a`
has addition:
```lean
instance [Add a] : Add (Array a) where
add x y := Array.zipWith x y (. + .)
#eval Add.add #[1, 2] #[3, 4]
-- #[4, 6]
#eval #[1, 2] + #[3, 4]
-- #[4, 6]
```
Note that `x + y` is notation for `Add.add x y` in Lean.
The example above demonstrates how type classes are used to overload notation.
Now, we explore another application. We often need an arbitrary element of a given type.
Recall that types may not have any elements in Lean.
It often happens that we would like a definition to return an arbitraryt element in a "corner case."
For example, we may like the expression ``head xs`` to be of type ``a`` when ``xs`` is of type ``List a``.
Similarly, many theorems hold under the additional assumption that a type is not empty.
For example, if ``a`` is a type, ``exists x : a, x = x`` is true only if ``a`` is not empty.
The standard library defines a type class ``Inhabited`` to enable type class inference to infer a
"default" or "arbitrary" element of an inhabited type.
Let us start with the first step of the program above, declaring an appropriate class:
```lean
# namespace Ex
class Inhabited (a : Type _) where
default : a
#check @Inhabited.default
-- Inhabited.default : {a : Type _} → [self : Inhabited a] → a
# end Ex
```
Note `Inhabited.default` doesn't have any explicit argument.
An element of the class ``Inhabited a`` is simply an expression of the form ``Inhabited.mk x``, for some element ``x : a``.
The projection ``Inhabited.default`` will allow us to "extract" such an element of ``a`` from an element of ``Inhabited a``.
Now we populate the class with some instances:
```lean
# namespace Ex
# class Inhabited (a : Type _) where
# default : a
instance : Inhabited Bool where
default := true
instance : Inhabited Nat where
default := 0
instance : Inhabited Unit where
default := ()
instance : Inhabited Prop where
default := True
#eval (Inhabited.default : Nat)
-- 0
#eval (Inhabited.default : Bool)
-- true
# end Ex
```
You can use the command `export` to create the alias `default` for `Inhabited.default`
```lean
# namespace Ex
# class Inhabited (a : Type _) where
# default : a
# instance : Inhabited Bool where
# default := true
# instance : Inhabited Nat where
# default := 0
# instance : Inhabited Unit where
# default := ()
# instance : Inhabited Prop where
# default := True
export Inhabited (default)
#eval (default : Nat)
-- 0
#eval (default : Bool)
-- true
# end Ex
```
Sometimes we want to think of the default element of a type as being an *arbitrary* element, whose specific value should not play a role in our proofs.
For that purpose, we can write ``arbitrary`` instead of ``default``. We define ``arbitrary`` as an *opaque* constant.
Opaque constants are never unfolded by the type checker.
```lean
# namespace Ex
# export Inhabited (default)
theorem defNatEq0 : (default : Nat) = 0 :=
rfl
constant arbitrary [Inhabited a] : a :=
Inhabited.default
-- theorem arbitraryNatEq0 : (arbitrary : Nat) = 0 :=
-- rfl
/-
error: type mismatch
rfl
has type
arbitrary = arbitrary
but is expected to have type
arbitrary = 0
-/
# end Ex
```
The theorem `defNatEq0` type checks because the type checker can unfold `(default : Nat)` and reduce it to `0`. This is not the case in the theorem `arbitraryNatEq0` because `arbitrary` is an opaque constant.
## Chaining Instances
If that were the extent of type class inference, it would not be all that impressive;
it would be simply a mechanism of storing a list of instances for the elaborator to find in a lookup table.
What makes type class inference powerful is that one can *chain* instances. That is,
an instance declaration can in turn depend on an implicit instance of a type class.
This causes class inference to chain through instances recursively, backtracking when necessary, in a Prolog-like search.
For example, the following definition shows that if two types ``a`` and ``b`` are inhabited, then so is their product:
```lean
instance [Inhabited a] [Inhabited b] : Inhabited (a × b) where
default := (arbitrary, arbitrary)
```
With this added to the earlier instance declarations, type class instance can infer, for example, a default element of ``Nat × Bool``:
```lean
# namespace Ex
# class Inhabited (a : Type _) where
# default : a
# instance : Inhabited Bool where
# default := true
# instance : Inhabited Nat where
# default := 0
# constant arbitrary [Inhabited a] : a :=
# Inhabited.default
instance [Inhabited a] [Inhabited b] : Inhabited (a × b) where
default := (arbitrary, arbitrary)
#eval (arbitrary : Nat × Bool)
-- (0, true)
# end Ex
```
Similarly, we can inhabit tyhe function with suitable constant functions:
```lean
instance [Inhabited b] : Inhabited (a -> b) where
default := fun _ => arbitrary
```
As an exercise, try defining default instances for other types, such as `List` and `Sum` types.

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Lean is a functional programming language that makes it easy to
write correct and maintainable code.
You can also use Lean as an interactive theorem prover.
Lean programming primarily involves defining types and functions.
This allows your focus to remain on the problem domain and manipulating its data,