{# LANGUAGE CPP #}
{# LANGUAGE PackageImports #}
#ifdef __HASTE__
import Haste.DOM
import Haste.Events
#endif
import Text.ParserCombinators.Parsec hiding (State)
import Text.Read hiding (get)
import Control.Arrow
import Control.Monad
import "mtl" Control.Monad.State  Haste has 2 versions of the State Monad.
Outcoding UNIX geniuses
Static types prevent disasters by catching bugs at compile time. Yet many languages have selfdefeating types. Type annotation can be so laborious that weary programmers give up and switch to unsafe languages.
Adding insult to injury, some of these prolix type systems are simultaneously inexpressive. Some lack parametric polymorphism, forcing programmers to duplicate code or subvert static typing with casts. A purist might escape this dilemma by writing code to generate code, but this leads to another set of problems.
Type theory shows how to avoid these pitfalls, but mainstream programmers seem unaware:

Popular authors Bruce Eckel and Robert C. Martin mistakenly believe strong typing implies verbosity, and worse still, testing conquers all. Tests are undoubtedly invaluable, but at best they “prove” by example. As in mathematics, the one true path lies in rigorous proofs of correctness. That is, we need strong static types so that logic can work its magic. One could even argue a testheavy approach helps attackers find exploits: the test cases you choose may hint at the bugs you overlooked.
Why is this so? Perhaps people think the theory is arcane, dry, and impractical?
By working through some programming interview questions, we’ll find the relevant theory is surprisingly accessible. We quickly arrive at a simple type inference or type reconstruction algorithm that seems too good to be true: it powers strongly typed languages that support parametric polymorphism without requiring any type declarations.
The above type inference demo is a bit ugly; our next interpreter will have a parser for a nice input language but for now we’ll make do without one. The default value of the input text area describes the abstract syntax trees of:
length length "hello" \x > x 2 \x > (+) x 42 \x > (+) (x 42) \x > x \x y > x \x y z > x z(y z)
Clicking the button infers their types:
String > Int Int Int > a > a Int > Int (Int > Int) > Int > Int a > a a > b > a (a > b > c) > (a > b) > a > c
Only the length and (+) functions have predefined types. An algorithm figures out the rest.
Before presenting the questions, let’s get some paperwork out of the way:
Lastly, to be fair to Go: for fullblown generics, we need algebraic data types and type operators to define, say, a binary tree containing values of any given type. Even then, parametric polymorphism is only half the problem. The other half is ad hoc polymorphism, which Haskell researchers only neatly solved in the late 1980s with type classes. Practical Haskell compilers also need more type trickery for unboxing.
1. Identifying twins
Determine if two binary trees of integers are equal.
Solution: I’d love to be asked this question so I could give the twoword answer deriving Eq:
data Tree a = Leaf a  Branch (Tree a) (Tree a) deriving Eq
Haskell’s derived instance feature automatically works on any algebraic data type built on any type for which equality makes sense. It even works for mutually recursive data types (see Data.Tree):
data Tree a = Node a (Forest a) deriving Eq data Forest a = Forest [Tree a] deriving Eq
Perhaps my interviewer would ask me to explain deriving Eq does. Roughly speaking, it generates code like the following, saving the programmer from stating the obvious:
data Tree a = Leaf a  Branch (Tree a) (Tree a) eq (Leaf x) (Leaf y) = x == y eq (Branch xl xr) (Branch yl yr) = eq xl yl && eq xr yr eq _ _ = False
2. On assignment
This time, one of the trees may contain variables in place of integers. Can we assign integers to all variables so the trees are equal? The same variable may appear more than once.
Solution: We extend our data structure to hold variables:
data Tree a = Var String  Leaf a  Branch (Tree a) (Tree a)
As before, we traverse both trees and look for nodes that differ in value or type. If one is a variable, then we record a constraint, that is, a variable assignment that is required for the trees to be equal such as a = 4 or b = 2. If there are conflicting values for the same variable, then we indicate failure by returning Nothing. Otherwise we return Just the list of assignments found.
solve (Leaf x) (Leaf y) as  x == y = Just as solve (Var v) (Leaf x) as = addConstraint v x as solve l@(Leaf _) r@(Var _) as = solve r l as solve (Branch xl xr) (Branch yl yr) as = solve xl yl as >>= solve xr yr solve _ _ _ = Nothing addConstraint v x cs = case lookup v cs of Nothing > Just $ (v, x):cs Just x'  x == x' > Just cs _ > Nothing
3. Both Sides, Now
Now suppose leaf nodes in both trees can hold integer variables. Can two trees be made equal by assigning certain integers to the variables? If so, find the most general solution.
Solution: We proceed as before, but now we may encounter constraints such as a = b, which equate two variables. To handle such a constraint, we pick one of the variables, such as a, and replace all occurrences of a with the other side, which is b in our example. This eliminates a from all constraints. Eventually, all our constraints have an integer on at least one side, which we check for consistency.
We discard redundant constraints where the same variable appears on both sides, such as a = a. Thus a variable may wind up with no integer assigned to it, which means if a solution exists, it can take any value.
For clarity, we separate the gathering of constraints from their unification. Lazy evaluation means these steps are actually interleaved, but our code will appear to solve the problem in two phases.
Also for clarity, our code is inefficient: it’s likely faster to maintain a Data.Map of substitutions, have each new substitution affect this map, and only apply the substitution at the last minute.
data Tree a = Var String  Leaf a  Branch (Tree a) (Tree a) deriving Show gather (Leaf x) (Leaf y)  x == y = Just [] gather (Branch xl xr) (Branch yl yr) = (++) <$> gather xl yl <*> gather xr yr gather (Var _) (Branch _ _) = Nothing gather x@(Var v) y = Just [(x, y)] gather x y@(Var _) = gather y x gather _ _ = Nothing unify acc [] = Just acc unify acc ((Leaf a, Leaf a') :rest)  a == a' = unify acc rest unify acc ((Var x , Var x') :rest)  x == x' = unify acc rest unify acc ((Var x , t) :rest) = unify ((x, t):acc) $ join (***) (sub x t) <$> rest unify acc ((t , v@(Var _)):rest) = unify acc ((v, t):rest) unify _ _ = Nothing sub x t a = case a of Var x'  x == x' > t Branch l r > Branch (sub x t l) (sub x t r) _ > a solve t u = unify [] =<< gather t u
The peppering of acc throughout the definition of unify is mildly irritating. We can remove a few with an explicit case statement (which is what happens behind the scenes anyway):
unify acc a = case a of [] > Just acc ((Leaf a, Leaf a') :rest)  a == a' > unify acc rest ((Var x , Var x') :rest)  x == x' > unify acc rest ((Var x , t) :rest) > unify ((x, t):acc) $ join (***) (sub x t) <$> rest ((t , v@(Var _)):rest) > unify acc ((v, t):rest) _ > Nothing
We’ll soon see a more thorough way to clean the code.
4. Once more, with subtrees
What if variables can represent subtrees?
Solution: Although we’ve significantly generalized the problem, our answer almost remains the same.
We remove one case from the gather function, as it is now legal to equate a variable to a subtree. Then we modfiy one case to the unify function: before we perform a substitution, we first check that our variable only appears on one side to avoid infinite recursion. Lastly, we add a case to unify when both sides are branches.
We take this opportunity to define unify using the state monad, which saves us from explicitly referring to the list of assignments found so far, that is, the list previously known as acc. To a first approximation, we’re employing macros to hide uninteresting code.
data Tree a = Var String  Leaf a  Branch (Tree a) (Tree a) deriving Show
treeSolve :: (Show a, Eq a) => Tree a > Tree a > Maybe [(String, Tree a)]
treeSolve t1 t2 = (`evalState` []) . unify =<< gather t1 t2 where
gather (Branch xl xr) (Branch yl yr) = (++) <$> gather xl yl <*> gather xr yr
gather (Leaf x) (Leaf y)  x == y = Just []
gather v@(Var _) x = Just [(v, x)]
gather t v@(Var _) = gather v t
gather _ _ = Nothing
unify :: Eq a =>
[(Tree a, Tree a)] > State [(String, Tree a)] (Maybe [(String, Tree a)])
unify [] = Just <$> get
unify ((Branch a b, Branch a' b'):rest) = unify $ (a, a'):(b, b'):rest
unify ((Leaf a, Leaf a'):rest)  a == a' = unify rest
unify ((Var x, Var x'):rest)  x == x' = unify rest
unify ((Var x, t):rest) = if twoSided t then pure Nothing
else modify ((x, t):) >> unify (join (***) (sub x t) <$> rest) where
twoSided (Branch l r) = twoSided l  twoSided r
twoSided (Var y)  x == y = True
twoSided _ = False
unify ((t, v@(Var _)):rest) = unify $ (v, t):rest
unify _ = pure Nothing
sub x t a = case a of Var x'  x == x' > t
Branch l r > Branch (sub x t l) (sub x t r)
_ > a
Here’s a demo of the above code:
The given example ought to be enough enough to understand the input format, which is parsed by the following:
treePair :: Parser (Tree Int, Tree Int)
treePair = do
spaces
t < tree
spaces
u < tree
spaces
eof
pure (t, u)
tree :: Read a => Parser (Tree a)
tree = tr where
tr = leaf <> branch
branch = between (char '(') (char ')') $ do
spaces
l < tr
spaces
r < tr
spaces
pure $ Branch l r
leaf = do
s < many1 alphaNum
pure $ case readMaybe s of
Nothing > Var s
Just a > Leaf a
5. Type inference!
Design a language based on lambda calculus where integers and strings are primitve types, and where we can deduce whether a given expression is typable, that is, whether types can be assigned to the untyped bindings so that the expression is welltyped. If so, find the most general type.
For example, the expression \f > f 2 which takes its first argument f and applies to the integer 2 must have type (Int > u) > u. Here, u is a type variable, that is, we can substitute u with any type. This is known as parametric polymorphism.
More precisely the inferred type is most general, or principal if:

Substituting types such as Int or (Int > Int) > Int (sometimes called type constants for clarity) for all the type variables results in a welltyped closed term.

There are no other ways of typing the given expression.
Solution: We define an abstract syntax tree for an expression in our language: applications, lambda abstractions, variables, integers, and strings:
infixl 5 :@
data Expr = Expr :@ Expr  Lam String Expr  V String  I Int  S String
deriving Read
So far we have simultaneously traversed two trees to generate constraints. This time, we traverse a single abstract syntax tree. The constraints we generate along the way equate types, which are represented with another data type:
infixr 5 :>
data Type = T String  Type :> Type  TV String deriving Show
The T constructor is for primitive data types, which are Int and String. The (:>) constructor is for functions, and the TV constructor is for constructing type variables.
The rules for building constraints from expressions are what we might expect:

The type of a primitive value is its corresponding type; for example, 5 has type T "Int" and "Hello, World" has type T "String".

For an application f x, we recursively determine the type tf of f and tx of x (possibly gathering new constraints along the way), generate a new type variable tfx to return and generate the constraint that tf is tx :> tfx.

For a lambda abstraction \x.t, we generate a new type variable tx to represent the type of x. Then we recursively find the type tt of t being careful to assign the type tx to any free occurrence of x, and return the type tx :> tt for the lambda.
Bookkeeping is fiddly. To guarantee a unique name for each type variable, we maintain a counter which we increment for each new name. We also maintain an environment gamma that records the types of variables in lambda abstractions.
We want more than assignments satisfying the constraints: we also want the type of the given expression. Accordingly, we modify gather to return the type of an expression as well as the type constraints it requires.
gather :: [(String, Type)] > Expr > State ([(Type, Type)], Int) Type
gather gamma expr = case expr of
I _ > pure $ T "Int"
S _ > pure $ T "String"
f :@ x > do
tf < gather gamma f
tx < gather gamma x
tfx < newTV
(cs, i) < get
put ((tf, tx :> tfx):cs, i)
pure tfx
V s > let Just tv = lookup s gamma in pure tv
Lam x t > do
tx < newTV
tt < gather ((x, tx):gamma) t
pure $ tx :> tt
where
newTV = do
(cs, i) < get
put (cs, i + 1)
pure $ TV $ 't':show i
We employ the same unification strategy:

If there are no constraints left, then we have successfully inferred the type.

If both sides have the form s > t for some type expressions s and t, then add two new constraints to the set: one equating the type expressions before the > type constructor, and the other equating those after.

If both sides of a constraint are the same, then we simply move on.

If one side is a type variable t, and t also appears somewhere on the other side, then we are attempting to create an infinite type, which is forbidden. Otherwise the constraint is something like t = u > (Int > u), and we substitute all occurences of t in the constraint set with the type expression on the other side.

If none of the above applies, then the given term is untypable.
unify :: [(Type, Type)] > State [(String, Type)] (Maybe [(String, Type)])
unify [] = Just <$> get
unify ((tx :> ty, ux :> uy):rest) = unify $ (tx, ux):(ty, uy):rest
unify ((T t, T u) :rest)  t == u = unify rest
unify ((TV v, TV w):rest)  v == w = unify rest
unify ((TV x, t) :rest) = if twoSided t then pure Nothing
else modify ((x, t):) >> unify (join (***) (sub (x, t)) <$> rest)
where
twoSided (t :> u) = twoSided t  twoSided u
twoSided (TV y)  x == y = True
twoSided _ = False
unify ((t, v@(TV _)):rest) = unify ((v, t):rest)
unify _ = pure Nothing
sub (x, t) y = case y of
TV x'  x == x' > t
a :> b > sub (x, t) a :> sub (x, t) b
_ > y
solve gamma x = foldr sub ty <$> evalState (unify cs) [] where
(ty, (cs, _)) = runState (gather gamma x) ([], 0)
This algorithm is known as Algorithm W, and is the heart of the HindleyMilner type system, or HM for short.
Example
Let’s walk through an example. The expression \f > f 2 would be represented as the Expr tree Lam "f" (V "f" :@ I 2). Calling gather on this tree consists of the following:

Generate a new type variable t.

Recursively invoke gather on the lambda body to find its type u, with the local constraint that the symbol f has type t.

Return the type t > u.
Step 2 expands to the following:

Recursively invoke gather on the left and right children of the (:@) node to find their types a and b.

Generate a new type variable c.

Add the global constraint that a has type b > c.

Return the type c.
In step 1, the left child is the symbol f, which has type t because of the local constraint generated by the Lam case, while the right child has type TInt because it is the integer constant 2. Neither child generates any more constraints.
Unification combines these constraints to find \f > f 2 has type (Int > u) > u.
UI
We predefine type signatures of certain functions:
prelude :: [(String, Type)]
prelude = [
("+", T "Int" :> T "Int" :> T "Int"),
("length", T "String" :> T "Int")]
These become the initial environment in our demo:
#ifdef __HASTE__
main = withElems ["treeIn", "treeOut", "treeB", "input", "output", "inferB"] $
\[treeIn, treeOut, treeB, iEl, oEl, inferB] > do
treeB `onEvent` Click $ const $ do
s < getProp treeIn "value"
case parse treePair "" s of
Left (err) > setProp treeOut "value" $ "parse error: " ++ show err
Right (t, u) > case treeSolve t u of
Nothing > setProp treeOut "value" "no solution"
Just as > setProp treeOut "value" $ unlines $ show <$> as
inferB `onEvent` Click $ const $ do
s < getProp iEl "value"
setProp oEl "value" $ unlines $ map (\xstr > case readMaybe xstr of
Nothing > "READ ERROR"
Just x > maybe "BAD TYPE" show (solve prelude x)) $ lines s
#else
main = do
print $ solve [] (Lam "x" (V "x" :@ I 2))
print $ solve prelude (Lam "x" (V "+" :@ V "x" :@ I 42))
#endif