interpret Parigot's lambda-mu calculus in Haskell - haskell

One can interpret the lambda calculus in Haskell:
data Expr = Var String | Lam String Expr | App Expr Expr
data Value a = V a | F (Value a -> Value a)
interpret :: [(String, Value a)] -> Expr -> Value a
interpret env (Var x) = case lookup x env of
Nothing -> error "undefined variable"
Just v -> v
interpret env (Lam x e) = F (\v -> interpret ((x, v):env) e)
interpret env (App e1 e2) = case interpret env e1 of
V _ -> error "not a function"
F f -> f (interpret env e2)
How could the above interpreter be extended to the lambda-mu calculus? My guess is that it should use continuations for interpreting the additional constructs in this calculus. (15) and (16) from the Bernardi&Moortgat paper are the kind of translations I expect.
It is possible since Haskell is Turing-complete, but how?
Hint: See the comment on page 197 on this research paper for the intuitive meaning of the mu binder.

Here's a mindless transliteration of the reduction rules from the paper, using #user2407038's representation (as you'll see, when I say mindless, I really do mean mindless):
{-# LANGUAGE DataKinds, KindSignatures, GADTs #-}
{-# LANGUAGE StandaloneDeriving #-}
import Control.Monad.Writer
import Control.Applicative
import Data.Monoid
data TermType = Named | Unnamed
type Var = String
type MuVar = String
data Expr (n :: TermType) where
Var :: Var -> Expr Unnamed
Lam :: Var -> Expr Unnamed -> Expr Unnamed
App :: Expr Unnamed -> Expr Unnamed -> Expr Unnamed
Freeze :: MuVar -> Expr Unnamed -> Expr Named
Mu :: MuVar -> Expr Named -> Expr Unnamed
deriving instance Show (Expr n)
substU :: Var -> Expr Unnamed -> Expr n -> Expr n
substU x e = go
where
go :: Expr n -> Expr n
go (Var y) = if y == x then e else Var y
go (Lam y e) = Lam y $ if y == x then e else go e
go (App f e) = App (go f) (go e)
go (Freeze alpha e) = Freeze alpha (go e)
go (Mu alpha u) = Mu alpha (go u)
renameN :: MuVar -> MuVar -> Expr n -> Expr n
renameN beta alpha = go
where
go :: Expr n -> Expr n
go (Var x) = Var x
go (Lam x e) = Lam x (go e)
go (App f e) = App (go f) (go e)
go (Freeze gamma e) = Freeze (if gamma == beta then alpha else gamma) (go e)
go (Mu gamma u) = Mu gamma $ if gamma == beta then u else go u
appN :: MuVar -> Expr Unnamed -> Expr n -> Expr n
appN beta v = go
where
go :: Expr n -> Expr n
go (Var x) = Var x
go (Lam x e) = Lam x (go e)
go (App f e) = App (go f) (go e)
go (Freeze alpha w) = Freeze alpha $ if alpha == beta then App (go w) v else go w
go (Mu alpha u) = Mu alpha $ if alpha /= beta then go u else u
reduceTo :: a -> Writer Any a
reduceTo x = tell (Any True) >> return x
reduce0 :: Expr n -> Writer Any (Expr n)
reduce0 (App (Lam x u) v) = reduceTo $ substU x v u
reduce0 (App (Mu beta u) v) = reduceTo $ Mu beta $ appN beta v u
reduce0 (Freeze alpha (Mu beta u)) = reduceTo $ renameN beta alpha u
reduce0 e = return e
reduce1 :: Expr n -> Writer Any (Expr n)
reduce1 (Var x) = return $ Var x
reduce1 (Lam x e) = reduce0 =<< (Lam x <$> reduce1 e)
reduce1 (App f e) = reduce0 =<< (App <$> reduce1 f <*> reduce1 e)
reduce1 (Freeze alpha e) = reduce0 =<< (Freeze alpha <$> reduce1 e)
reduce1 (Mu alpha u) = reduce0 =<< (Mu alpha <$> reduce1 u)
reduce :: Expr n -> Expr n
reduce e = case runWriter (reduce1 e) of
(e', Any changed) -> if changed then reduce e' else e
It "works" for the example from the paper: with
example 0 = App (App t (Var "x")) (Var "y")
where
t = Lam "x" $ Lam "y" $ Mu "delta" $ Freeze "phi" $ App (Var "x") (Var "y")
example n = App (example (n-1)) (Var ("z_" ++ show n))
I can reduce example n to the expected result:
*Main> reduce (example 10)
Mu "delta" (Freeze "phi" (App (Var "x") (Var "y")))
The reason I put scare quotes around "works" above is that I have no intuition about the λμ calculus so I don't know what it should do.

Note: this is only a partial answer since I'm not sure how to extend the interpreter.
This seems like a good use case for DataKinds. The Expr datatype is indexed on a type which is named or unnamed. The regular lambda constructs produce named terms only.
{-# LANGUAGE GADTs, DataKinds, KindSignatures #-}
data TermType = Named | Unnamed
type Var = String
type MuVar = String
data Expr (n :: TermType) where
Var :: Var -> Expr Unnamed
Lam :: Var -> Expr Unnamed -> Expr Unnamed
App :: Expr Unnamed -> Expr Unnamed -> Expr Unnamed
and the additional Mu and Name constructs can manipulate the TermType.
...
Name :: MuVar -> Expr Unnamed -> Expr Named
Mu :: MuVar -> Expr Named -> Expr Unnamed

How about something like the below. I don't have a good idea on how to traverse Value a, but at least I can see it evaluates example n into MuV.
import Data.Maybe
type Var = String
type MuVar = String
data Expr = Var Var
| Lam Var Expr
| App Expr Expr
| Mu MuVar MuVar Expr
deriving Show
data Value a = ConV a
| LamV (Value a -> Value a)
| MuV (Value a -> Value a)
type Env a = [(Var, Value a)]
type MuEnv a = [(MuVar, Value a -> Value a)]
varScopeErr :: Var -> Value a
varScopeErr v = error $ unwords ["Out of scope λ variable:", show v]
appErr :: Value a
appErr = error "Trying to apply a non-lambda"
muVarScopeErr :: MuVar -> (Value a -> Value a)
muVarScopeErr alpha = id
app :: Value a -> Value a -> Value a
app (LamV f) x = f x
app (MuV f) x = MuV $ \y -> f x `app` y
app _ _ = appErr
eval :: Env a -> MuEnv a -> Expr -> Value a
eval env menv (Var v) = fromMaybe (varScopeErr v) $ lookup v env
eval env menv (Lam v e) = LamV $ \x -> eval ((v, x):env) menv e
eval env menv (Mu alpha beta e) = MuV $ \u ->
let menv' = (alpha, (`app` u)):menv
wrap = fromMaybe (muVarScopeErr beta) $ lookup beta menv'
in wrap (eval env menv' e)
eval env menv (App f e) = eval env menv f `app` eval env menv e
example 0 = App (App t (Var "v")) (Var "w")
where
t = Lam "x" $ Lam "y" $ Mu "delta" "phi" $ App (Var "x") (Var "y")
example n = App (example (n-1)) (Var ("z_" ++ show n))

Related

removing explicit recursion by replacing catamorphism

I have this AST data structure
data AST = Integer Int
| Let String AST AST
| Plus AST AST
| Minus AST AST
| Times AST AST
| Variable String
| Boolean Bool
| If AST AST AST
| Lambda String AST Type Type
| Application AST AST
| And AST AST
| Or AST AST
| Quot AST AST
| Rem AST AST
| Negate AST
| Eq AST AST
| Leq AST AST
| Geq AST AST
| Neq AST AST
| Lt AST AST
| Gt AST AST
and this evaluation code:
eval :: AST -> AST
eval = cata go where
go :: ASTF (AST) -> AST
go (LetF var e e') = eval $ substVar (var, e) e'
go (PlusF (Integer n) (Integer m)) = Integer (n + m)
go (MinusF (Integer n) (Integer m)) = Integer (n - m)
go (TimesF (Integer n) (Integer m)) = Integer (n * m)
go (QuotF (Integer n) (Integer m)) = Integer (quot n m)
go (RemF (Integer n) (Integer m)) = Integer (rem n m)
go (IfF (Boolean b) e e') = if b then e else e'
go (ApplicationF (Lambda var e _ _) e') = eval $ substVar (var, e') e
go (AndF (Boolean b) (Boolean b')) = Boolean (b && b')
go (OrF (Boolean b) (Boolean b')) = Boolean (b || b')
go (NegateF (Boolean b)) = Boolean (not b)
go (EqF e e') = Boolean (e == e')
go (NeqF e e') = Boolean (e /= e')
go (LeqF (Integer n) (Integer m)) = Boolean (n <= m)
go (GeqF (Integer n) (Integer m)) = Boolean (n >= m)
go (LtF (Integer n) (Integer m)) = Boolean (n < m)
go (GtF (Integer n) (Integer m)) = Boolean (n > m)
go astf = embed astf
I feel like there should be away to remove the explicit recursion for 'let' and application, but I'm not sure exactly which recursion scheme I should reach for. Which recursion scheme(s) can be used to remove recursion in this case and similar cases and do you have any good ways of identifying situations where said recursion scheme is applicable?
eval isn't directly expressible as a catamorphism because eval (Let x e e') applies eval to subst (x, eval e) e', which is not a subterm of Let x e e' (e or e'). Instead, consider the composition of eval and substitution. If you generalize substitution subst to substitute many variables at once with substs, then you can get the following equation:
(eval . substs s) (Let x e e')
= eval (Let x (substs s e) (substs s e'))
= (eval . subst (x, (eval . substs s) e) . substs s) e'
-- by (subst xe . substs s) = substs (xe : s)
= (eval . substs ((x, (eval . substs s) e) : s)) e'
where we do have a function of the form (eval . substs _) applied to both subterms e and e'. In order to account for the parameter of substs, you can use cata with an F-algebra where the carrier is a function ASTF (Sub -> AST) -> Sub -> AST, then you get to pass a different substitution Sub to each subterm.
{-# LANGUAGE TemplateHaskell, TypeFamilies #-}
import Data.Functor.Foldable (cata)
import Data.Functor.Foldable.TH (makeBaseFunctor)
import Data.Maybe (fromJust)
type Id = String
data AST
= Let Id AST AST
| Var Id
| Int Int
| Plus AST AST
type Sub = [(Id, AST)]
makeBaseFunctor ''AST
evalSubsts :: AST -> Sub -> AST
evalSubsts = cata go where
go (LetF x e e') s = e' ((x, e s) : s)
go (VarF x) s = fromJust (lookup x s)
go (IntF n) _ = Int n
go (PlusF e e') s =
let (Int n, Int m) = (e s, e' s) in
Int (n + m)
-- Or this:
-- go (PlusF e e') = liftA2 plus e e'
-- where plus (Int n) (Int m) = Int (n + m)
eval :: AST -> AST
eval e = evalSubsts e []
Another way to think of evalSubsts is as an evaluator using an environment, mapping variables to values. Then to bind a variable, rather than to do a substitution, is just to insert its value in the environment, which gets looked up once you reach a Var node.
Continuing on the evalSubsts approach from #li-yao-xia, let's try to add lambdas and applications to our language. We start by extending AST:
data AST
...
| Lam Id AST
| App AST AST
But, how do we write our LamF case in evalSubst?
go (LamF x e) s = ???
We want our lambdas to be statically (lexically) scoped, so we need to keep around the environment s, but we also can't possibly apply an environment to e yet because we don't know what the value for x should be. We're in a bind!
The solution here is to recognize that, while AST is a great representation for our input, it is not a great representation for the output. Indeed, it's a bit of a coincidence that input Ints and output Ints both happen to share the same structure, and for lambdas, perhaps they shouldn't. So, we can make a new output representation:
data Val
= IntV Int
| LamV (Val -> Val)
type Sub = [(Id, Val)]
The key here is that LamV is a function, not simply some data. With this, we can finish our definition of evalSubsts:
evalSubsts :: AST -> Sub -> Val
evalSubsts = cata go where
go (LetF x e e') s = e' ((x, e s) : s)
go (VarF x) s = fromJust (lookup x s)
go (IntF n) _ = IntV n
go (LamF x e) s = LamV $ \xVal -> e ((x, xVal) : s)
go (AppF lam e) s =
let (LamV f) = lam s in f (e s)
go (PlusF e e') s =
let (IntV n, IntV m) = (e s, e' s) in
IntV (n + m)

Expression expansion using recursion schemes

I have a data type representing arithmetic expressions:
data E = Add E E | Mul E E | Var String
I want to write an expansion function which will convert an expression into sum of products of variables (sort of braces expansion). Using recursion schemes of course.
I only could think of an algorithm in the spirit of "progress and preservation". The algorithm at each step constructs terms that are fully expanded so there is no need to re-check.
The handling of Mul made me crazy, so instead of doing it directly I used an isomorphic type of [[String]] and took advantage of concat and concatMap already implemented for me:
type Poly = [Mono]
type Mono = [String]
mulMonoBy :: Mono -> Poly -> Poly
mulMonoBy x = map (x ++)
mulPoly :: Poly -> Poly -> Poly
mulPoly x = concatMap (flip mulMonoBy x)
So then I just use cata:
expandList :: E -> Poly
expandList = cata $ \case
Var x -> [[x]]
Add e1 e2 = e1 ++ e2
Mul e1 e2 = mulPoly e1 e2
And convert back:
fromPoly :: Poly -> Expr
fromPoly = foldr1 Add . map fromMono where
fromMono = foldr1 Mul . map Var
Are there significantly better approaches?
Upd: There are few confusions.
The solution does allow multiline variable names. Add (Val "foo" (Mul (Val "foo) (Var "bar"))) is a representation of foo + foo * bar. I'm not representing x*y*z with Val "xyz" or something. Note that also as there are no scalars repeated vars such as "foo * foo * quux" are perfectly allowed.
By sum of products I mean sort of "curried" n-ary sum of products. A concise definition of sum of products is that I want an expression without any parentheses, with all parens represented by associativity and priority.
So (foo * bar + bar) + (foo * bar + bar) is not a sum of products as the because of middle + is sum of sums
(foo * bar + (bar + (foo * bar + bar))) or corresponding left-associative version are right answers, although we must guarantee that associativity is always left of always right. So the correct type for right-assoaciative solution is
data Poly = Sum Mono Poly
| Product Mono
which is isomorphic to nonempty lists: NonEmpty Poly (note Sum Mono Poly instead of Sum Poly Poly). If we allow empty sums or products then we get just the list of list representation I used.
Also of you don't care about performance, the multiplication seems to be just liftA2 (++)
I am no expert in recursion schemes, but since it sounds like you are trying to practice them, hopefully you will not find it too onerous to convert a solution using manual recursion to one using recursion schemes. I'll write it with mixed prose and code first, and include the complete code again at the end for simpler copy/pasting.
It is not too difficult to do using simply the distributive property and a bit of recursive algebra. Before we begin, though, let's define a better result type, one that guarantees we can only ever represent sums of products:
data Poly term = Sum (Poly term) (Poly term)
| Product (Mono term)
deriving Show
data Mono term = Term term
| MonoMul (Mono term) (Mono term)
deriving Show
This way we can't possibly mess up and accidentally yield an incorrect result like
(Mul (Var "x") (Add (Var "y") (Var "z")))
Now, let's write our function.
expand :: E -> Poly String
First, a base case: it is trivial to expand a Var, because it is already in sum-of-products form. But we must convert it a bit to fit it into our Poly result type:
expand (Var x) = Product (Term x)
Next, note that it is easy to expand an addition: simply expand the two sub-expressions, and add them together.
expand (Add x y) = Sum (expand x) (expand y)
What about a multiplication? That is a bit more complicated, since
Product (expand x) (expand y)
is ill-typed: we can't multiply polynomials, only monomials. But we do know how to do algebraic manipulation to turn a multiplication of polynomials into a sum of multiplications of monomials, via the distributive rule. As in your question, we'll need a function mulPoly. But let's just assume that exists, and implement it later.
expand (Mul x y) = mulPoly (expand x) (expand y)
That handles all the cases, so all that's left is to implement mulPoly by distributing the multiplications across the two polynomials' terms. We simply break down one of the polynomials one term at a time, and multiply the term across each of the terms in the other polynomial, adding together the results.
mulPoly :: Poly String -> Poly String -> Poly String
mulPoly (Product x) y = mulMonoBy x y
mulPoly (Sum a b) x = Sum (mulPoly a x) (mulPoly b x)
mulMonoBy :: Mono String -> Poly -> Poly
mulMonoBy x (Product y) = Product $ MonoMul x y
mulMonoBy x (Sum a b) = Sum (mulPoly a x') (mulPoly b x')
where x' = Product x
And in the end, we can test that it works as intended:
expand (Mul (Add (Var "a") (Var "b")) (Add (Var "y") (Var "z")))
{- results in: Sum (Sum (Product (MonoMul (Term "y") (Term "a")))
(Product (MonoMul (Term "z") (Term "a"))))
(Sum (Product (MonoMul (Term "y") (Term "b")))
(Product (MonoMul (Term "z") (Term "b"))))
-}
Or,
(a + b)(y * z) = ay + az + by + bz
which we know to be correct.
The complete solution, as promised above:
data E = Add E E | Mul E E | Var String
data Poly term = Sum (Poly term) (Poly term)
| Product (Mono term)
deriving Show
data Mono term = Term term
| MonoMul (Mono term) (Mono term)
deriving Show
expand :: E -> Poly String
expand (Var x) = Product (Term x)
expand (Add x y) = Sum (expand x) (expand y)
expand (Mul x y) = mulPoly (expand x) (expand y)
mulPoly :: Poly String -> Poly String -> Poly String
mulPoly (Product x) y = mulMonoBy x y
mulPoly (Sum a b) x = Sum (mulPoly a x) (mulPoly b x)
mulMonoBy :: Mono String -> Poly String -> Poly String
mulMonoBy x (Product y) = Product $ MonoMul x y
mulMonoBy x (Sum a b) = Sum (mulPoly a x') (mulPoly b x')
where x' = Product x
main = print $ expand (Mul (Add (Var "a") (Var "b")) (Add (Var "y") (Var "z")))
This answer has three sections. The first section, a summary in which I present my two favourite solutions, is the most important one. The second section contains types and imports, as well as extended commentary on the way towards the solutions. The third section focuses on the task of reassociating expressions, something that the original version of the answer (i.e. the second section) had not given due attention.
At the end of the day, I ended up with two solutions worth discussing. The first one is expandDirect (cf. the third section):
expandDirect :: E a -> E a
expandDirect = cata alg
where
alg = \case
Var' s -> Var s
Add' x y -> apo coalgAdd (Add x y)
Mul' x y -> (apo coalgAdd' . apo coalgMul) (Mul x y)
coalgAdd = \case
Add (Add x x') y -> Add' (Left x) (Right (Add x' y))
x -> Left <$> project x
coalgAdd' = \case
Add (Add x x') y -> Add' (Left x) (Right (Add x' y))
Add x (Add y y') -> Add' (Left x) (Right (Add y y'))
x -> Left <$> project x
coalgMul = \case
Mul (Add x x') y -> Add' (Right (Mul x y)) (Right (Mul x' y))
Mul x (Add y y') -> Add' (Right (Mul x y)) (Right (Mul x y'))
x -> Left <$> project x
With it, we rebuild the tree from the bottom (cata). On every branch, if we find something invalid we walk back and rewrite the subtree (apo), redistributing and reassociating as needed until all immediate children are correctly arranged (apo makes it possible to do that without having to rewrite everyting down to the very bottom).
The second solution, expandMeta, is a much simplified version of expandFlat from the third section.
expandMeta :: E a -> E a
expandMeta = apo coalg . cata alg
where
alg = \case
Var' s -> pure (Var s)
Add' x y -> x <> y
Mul' x y -> Mul <$> x <*> y
coalg = \case
x :| [] -> Left <$> project x
x :| (y:ys) -> Add' (Left x) (Right (y :| ys))
expandMeta is a metamorphism; that is, a catamorphism followed by an anamorphism (while we are using apo here as well, an apomorphism is just a fancy kind of anamorphism, so I guess the nomenclature still applies). The catamorphism changes the tree into a non-empty list -- that implicitly handles the reassociation of the Adds -- with the list applicative being used to distribute multiplication (much like you suggest). The coalgebra then quite trivially converts the non-empty list back into a tree with the appropriate shape.
Thank you for the question -- I had a lot of fun with it! Preliminaries:
{-# LANGUAGE LambdaCase #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE DeriveFunctor #-}
{-# LANGUAGE DeriveFoldable #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
import Data.Functor.Foldable
import qualified Data.List.NonEmpty as N
import Data.List.NonEmpty (NonEmpty(..))
import Data.Semigroup
import Data.Foldable (toList)
import Data.List (nub)
import qualified Data.Map as M
import Data.Map (Map, (!))
import Test.QuickCheck
data E a = Var a | Add (E a) (E a) | Mul (E a) (E a)
deriving (Eq, Show, Functor, Foldable)
data EF a b = Var' a | Add' b b | Mul' b b
deriving (Eq, Show, Functor)
type instance Base (E a) = EF a
instance Recursive (E a) where
project = \case
Var x -> Var' x
Add x y -> Add' x y
Mul x y -> Mul' x y
instance Corecursive (E a) where
embed = \case
Var' x -> Var x
Add' x y -> Add x y
Mul' x y -> Mul x y
To begin with, my first working (if flawed) attempt, which uses the applicative instance of (non-empty) lists to distribute:
expandTooClever :: E a -> E a
expandTooClever = cata $ \case
Var' s -> Var s
Add' x y -> Add x y
Mul' x y -> foldr1 Add (Mul <$> flatten x <*> flatten y)
where
flatten :: E a -> NonEmpty (E a)
flatten = cata $ \case
Var' s -> pure (Var s)
Add' x y -> x <> y
Mul' x y -> pure (foldr1 Mul (x <> y))
expandTooClever has one relatively serious problem: as it calls flatten, a full-blown fold, for both subtrees whenever it reaches a Mul, it has horrible asymptotics for chains of Mul.
Brute force, simplest-thing-that-could-possibly-work solution, with an algebra that calls itself recursively:
expandBrute :: E a -> E a
expandBrute = cata alg
where
alg = \case
Var' s -> Var s
Add' x y -> Add x y
Mul' (Add x x') y -> Add (alg (Mul' x y)) (alg (Mul' x' y))
Mul' x (Add y y') -> Add (alg (Mul' x y)) (alg (Mul' x y'))
Mul' x y -> Mul x y
The recursive calls are needed because the distribution might introduce new occurrences of Add under Mul.
A slightly more tasteful variant of expandBrute, with the recursive call factored out into a separate function:
expandNotSoBrute :: E a -> E a
expandNotSoBrute = cata alg
where
alg = \case
Var' s -> Var s
Add' x y -> Add x y
Mul' x y -> dis x y
dis (Add x x') y = Add (dis x y) (dis x' y)
dis x (Add y y') = Add (dis x y) (dis x y')
dis x y = Mul x y
A tamed expandNotSoBrute, with dis being turned into an apomorphism. This way of phrasing it expresses nicely the big picture of what is going on: if you only have Vars and Adds, you can trivially reproduce the tree bottom-up without a care in the world; if you hit a Mul, however, you have to go back and reconstuct the whole subtree to perform the distributions (I wonder is there is a specialised recursion scheme that captures this pattern).
expandEvert :: E a -> E a
expandEvert = cata alg
where
alg :: EF a (E a) -> E a
alg = \case
Var' s -> Var s
Add' x y -> Add x y
Mul' x y -> apo coalg (x, y)
coalg :: (E a, E a) -> EF a (Either (E a) (E a, E a))
coalg (Add x x', y) = Add' (Right (x, y)) (Right (x', y))
coalg (x, Add y y') = Add' (Right (x, y)) (Right (x, y'))
coalg (x, y) = Mul' (Left x) (Left y)
apo is necessary because we want to anticipate the final result if there is nothing else to distribute. (There is a way to write it with ana; however, that requires wastefully rebuilding trees of Muls without changes, which leads to the same asymptotics problem expandTooClever had.)
Last, but not least, a solution which is both a successful realisation of what I had attempted with expandTooClever and my interpretation of amalloy's answer. BT is a garden-variety binary tree with values on the leaves. A product is represented by a BT a, while a sum of products is a tree of trees.
expandSOP :: E a -> E a
expandSOP = cata algS . fmap (cata algP) . cata algSOP
where
algSOP :: EF a (BT (BT a)) -> BT (BT a)
algSOP = \case
Var' s -> pure (pure s)
Add' x y -> x <> y
Mul' x y -> (<>) <$> x <*> y
algP :: BTF a (E a) -> E a
algP = \case
Leaf' s -> Var s
Branch' x y -> Mul x y
algS :: BTF (E a) (E a) -> E a
algS = \case
Leaf' x -> x
Branch' x y -> Add x y
BT and its instances:
data BT a = Leaf a | Branch (BT a) (BT a)
deriving (Eq, Show)
data BTF a b = Leaf' a | Branch' b b
deriving (Eq, Show, Functor)
type instance Base (BT a) = BTF a
instance Recursive (BT a) where
project (Leaf s) = Leaf' s
project (Branch l r) = Branch' l r
instance Corecursive (BT a) where
embed (Leaf' s) = Leaf s
embed (Branch' l r) = Branch l r
instance Semigroup (BT a) where
l <> r = Branch l r
-- Writing this, as opposed to deriving it, for the sake of illustration.
instance Functor BT where
fmap f = cata $ \case
Leaf' x -> Leaf (f x)
Branch' l r -> Branch l r
instance Applicative BT where
pure x = Leaf x
u <*> v = ana coalg (u, v)
where
coalg = \case
(Leaf f, Leaf x) -> Leaf' (f x)
(Leaf f, Branch xl xr) -> Branch' (Leaf f, xl) (Leaf f, xr)
(Branch fl fr, v) -> Branch' (fl, v) (fr, v)
To wrap things up, a test suite:
newtype TestE = TestE { getTestE :: E Char }
deriving (Eq, Show)
instance Arbitrary TestE where
arbitrary = TestE <$> sized genExpr
where
genVar = Var <$> choose ('a', 'z')
genAdd n = Add <$> genSub n <*> genSub n
genMul n = Mul <$> genSub n <*> genSub n
genSub n = genExpr (n `div` 2)
genExpr = \case
0 -> genVar
n -> oneof [genVar, genAdd n, genMul n]
data TestRig b = TestRig (Map Char b) (E Char)
deriving (Show)
instance Arbitrary b => Arbitrary (TestRig b) where
arbitrary = do
e <- genExpr
d <- genDict e
return (TestRig d e)
where
genExpr = getTestE <$> arbitrary
genDict x = M.fromList . zip (keys x) <$> (infiniteListOf arbitrary)
keys = nub . toList
unsafeSubst :: Ord a => Map a b -> E a -> E b
unsafeSubst dict = fmap (dict !)
eval :: Num a => E a -> a
eval = cata $ \case
Var' x -> x
Add' x y -> x + y
Mul' x y -> x * y
evalRig :: (E Char -> E Char) -> TestRig Integer -> Integer
evalRig f (TestRig d e) = eval (unsafeSubst d (f e))
mkPropEval :: (E Char -> E Char) -> TestRig Integer -> Bool
mkPropEval f = (==) <$> evalRig id <*> evalRig f
isDistributed :: E a -> Bool
isDistributed = para $ \case
Add' (_, x) (_, y) -> x && y
Mul' (Add _ _, _) _ -> False
Mul' _ (Add _ _, _) -> False
Mul' (_, x) (_, y) -> x && y
_ -> True
mkPropDist :: (E Char -> E Char) -> TestE -> Bool
mkPropDist f = isDistributed . f . getTestE
main = mapM_ test
[ ("expandTooClever" , expandTooClever)
, ("expandBrute" , expandBrute)
, ("expandNotSoBrute", expandNotSoBrute)
, ("expandEvert" , expandEvert)
, ("expandSOP" , expandSOP)
]
where
test (header, func) = do
putStrLn $ "Testing: " ++ header
putStr "Evaluation test: "
quickCheck $ mkPropEval func
putStr "Distribution test: "
quickCheck $ mkPropDist func
By sum of products I mean sort of "curried" n-ary sum of products. A concise definition of sum of products is that I want an expression without any parentheses, with all parens represented by associativity and priority.
We can adjust the solutions above so that the sums are reassociated. The easiest way is replacing the outer BT in expandSOP with NonEmpty. Given that the multiplication there is, much like you suggest, liftA2 (<>), this works straight away.
expandFlat :: E a -> E a
expandFlat = cata algS . fmap (cata algP) . cata algSOP
where
algSOP :: EF a (NonEmpty (BT a)) -> NonEmpty (BT a)
algSOP = \case
Var' s -> pure (Leaf s)
Add' x y -> x <> y
Mul' x y -> (<>) <$> x <*> y
algP :: BTF a (E a) -> E a
algP = \case
Leaf' s -> Var s
Branch' x y -> Mul x y
algS :: NonEmptyF (E a) (E a) -> E a
algS = \case
NonEmptyF x Nothing -> x
NonEmptyF x (Just y) -> Add x y
Another option is using any of the other solutions and reassociating the sums in the distributed tree in a separate step.
flattenSum :: E a -> E a
flattenSum = cata alg
where
alg = \case
Add' x y -> apo coalg (x, y)
x -> embed x
coalg = \case
(Add x x', y) -> Add' (Left x) (Right (x', y))
(x, y) -> Add' (Left x) (Left y)
We can also roll flattenSum and expandEvert into a single function. Note that the sum coalgebra needs an extra case when it gets the result of the distribution coalgebra. That happens because, as the coalgebra proceeds from top to bottom, we can't be sure that the subtrees it generates are properly associated.
-- This is written in a slightly different style than the previous functions.
expandDirect :: E a -> E a
expandDirect = cata alg
where
alg = \case
Var' s -> Var s
Add' x y -> apo coalgAdd (Add x y)
Mul' x y -> (apo coalgAdd' . apo coalgMul) (Mul x y)
coalgAdd = \case
Add (Add x x') y -> Add' (Left x) (Right (Add x' y))
x -> Left <$> project x
coalgAdd' = \case
Add (Add x x') y -> Add' (Left x) (Right (Add x' y))
Add x (Add y y') -> Add' (Left x) (Right (Add y y'))
x -> Left <$> project x
coalgMul = \case
Mul (Add x x') y -> Add' (Right (Mul x y)) (Right (Mul x' y))
Mul x (Add y y') -> Add' (Right (Mul x y)) (Right (Mul x y'))
x -> Left <$> project x
Perhaps there is a more clever way of writing expandDirect, but I haven't figured it out yet.

Generalizing traversal of expressions with changes on specific nodes

I'm reading the lectures from the CIS194 course (version fall 2014) and I
wanted to have some comments on my solution to the Exercise 7 of the
Homework 5.
Here is the question:
Exercise 7 (Optional) The distribute and squashMulId functions are
quite similar, in that they traverse over the whole expression to make
changes to specific nodes. Generalize this notion, so that the two
functions can concentrate on just the bit that they need to transform.
(You can see the whole homework here: http://www.seas.upenn.edu/~cis194/fall14/hw/05-type-classes.pdf)
Here is my squashMulId function from Exercise 6:
squashMulId :: (Eq a, Ring a) => RingExpr a -> RingExpr a
squashMulId AddId = AddId
squashMulId MulId = MulId
squashMulId (Lit n) = Lit n
squashMulId (AddInv x) = AddInv (squashMulId x)
squashMulId (Add x y) = Add (squashMulId x) (squashMulId y)
squashMulId (Mul x (Lit y))
| y == mulId = squashMulId x
squashMulId (Mul (Lit x) y)
| x == mulId = squashMulId y
squashMulId (Mul x y) = Mul (squashMulId x) (squashMulId y)
And here is my solution to Exercise 7:
distribute :: RingExpr a -> RingExpr a
distribute = transform distribute'
where distribute' (Mul x (Add y z)) = Just $ Add (Mul x y) (Mul x z)
distribute' (Mul (Add x y) z) = Just $ Add (Mul x z) (Mul y z)
distribute' _ = Nothing
squashMulId :: (Eq a, Ring a) => RingExpr a -> RingExpr a
squashMulId = transform simplifyMul
where simplifyMul (Mul x (Lit y))
| y == mulId = Just $ squashMulId x
simplifyMul (Mul (Lit x) y)
| x == mulId = Just $ squashMulId y
simplifyMul _ = Nothing
transform :: (RingExpr a -> Maybe (RingExpr a)) -> RingExpr a -> RingExpr a
transform f e
| Just expr <- f e = expr
transform _ AddId = AddId
transform _ MulId = MulId
transform _ e#(Lit n) = e
transform f (AddInv x) = AddInv (transform f x)
transform f (Add x y) = Add (transform f x) (transform f y)
transform f (Mul x y) = Mul (transform f x) (transform f y)
Is there a better way of doing such generalization?
Your transform function is a very good start at handling general transformations of ASTs. I'm going to show you something closely related that's a little bit more general.
The uniplate library defines the following class for describing simple abstract syntax trees. An instance of the class needs only provide a definition for uniplate which should perform a step of transformation, possibly with side-effects, to the immediate decedents of the node.
import Control.Applicative
import Control.Monad.Identity
class Uniplate a where
uniplate :: Applicative m => a -> (a -> m a) -> m a
descend :: (a -> a) -> a -> a
descend f x = runIdentity $ descendM (pure . f) x
descendM :: Applicative m => (a -> m a) -> a -> m a
descendM = flip uniplate
A postorder transformation of the entire expression is defined for any type with a Uniplate instance.
transform :: Uniplate a => (a -> a) -> a -> a
transform f = f . descend (transform f)
My guess for the definitions of RingExpr and Ring (the link to the homework exercise is broken) are
data RingExpr a
= AddId
| MulId
| Lit a
| AddInv (RingExpr a)
| Add (RingExpr a) (RingExpr a)
| Mul (RingExpr a) (RingExpr a)
deriving Show
class Ring a where
addId :: a
mulId :: a
addInv :: a -> a
add :: a -> a -> a
mul :: a -> a -> a
We can define uniplate for any RingExpr a. For the three expressions that have subexpressions, AddInv, Add, and Mul, we perform the transformation p on each subexpression, then put the expression back together again in the Applicative using <$> (an infix version of fmap), and <*>. For the remaining expressions that have no sub expressions, we just package them back up in the Applicative using pure.
instance Uniplate (RingExpr a) where
uniplate e p = case e of
AddInv x -> AddInv <$> p x
Add x y -> Add <$> p x <*> p y
Mul x y -> Mul <$> p x <*> p y
_ -> pure e
The transform function we get from the Uniplate instance for RingExpr a is exactly the same as yours, except the Maybe return type wasn't necessary. A function that didn't want to change an expression could have simply returned the expression unchanged.
Here's my swing at writing squashMulId. I've split replacing the literals out into a separate function to make things look cleaner.
replaceIds :: (Eq a, Ring a) => RingExpr a -> RingExpr a
replaceIds (Lit n) | n == addId = AddId
replaceIds (Lit n) | n == mulId = MulId
replaceIds e = e
simplifyMul :: RingExpr a -> RingExpr a
simplifyMul (Mul x MulId) = x
simplifyMul (Mul MulId x ) = x
simplifyMul e = e
squashMulId :: (Eq a, Ring a) => RingExpr a -> RingExpr a
squashMulId = transform (simplifyMul . replaceIds)
This works for a simple example
> squashMulId . Mul (Lit True) . Add (Lit False) $ Lit True
Add AddId MulId

SAT solving with haskell SBV library: how to generate a predicate from a parsed string?

I want to parse a String that depicts a propositional formula and then find all models of the propositional formula with a SAT solver.
Now I can parse a propositional formula with the hatt package; see the testParse function below.
I can also run a SAT solver call with the SBV library; see the testParse function below.
Question:
How do I, at runtime, generate a value of type Predicate like myPredicate within the SBV library that represents the propositional formula I just parsed from a String? I only know how to manually type the forSome_ $ \x y z -> ... expression, but not how to write a converter function from an Expr value to a value of type Predicate.
-- cabal install sbv hatt
import Data.Logic.Propositional
import Data.SBV
-- Random test formula:
-- (x or ~z) and (y or ~z)
-- graphical depiction, see: https://www.wolframalpha.com/input/?i=%28x+or+~z%29+and+%28y+or+~z%29
testParse = parseExpr "test source" "((X | ~Z) & (Y | ~Z))"
myPredicate :: Predicate
myPredicate = forSome_ $ \x y z -> ((x :: SBool) ||| (bnot z)) &&& (y ||| (bnot z))
testSat = do
x <- allSat $ myPredicate
putStrLn $ show x
main = do
putStrLn $ show $ testParse
testSat
{-
Need a function that dynamically creates a Predicate
(as I did with the function (like "\x y z -> ..") for an arbitrary expression of type "Expr" that is parsed from String.
-}
Information that might be helpful:
Here is the link to the BitVectors.Data:
http://hackage.haskell.org/package/sbv-3.0/docs/src/Data-SBV-BitVectors-Data.html
Here is example code form Examples.Puzzles.PowerSet:
import Data.SBV
genPowerSet :: [SBool] -> SBool
genPowerSet = bAll isBool
where isBool x = x .== true ||| x .== false
powerSet :: [Word8] -> IO ()
powerSet xs = do putStrLn $ "Finding all subsets of " ++ show xs
res <- allSat $ genPowerSet `fmap` mkExistVars n
Here is the Expr data type (from hatt library):
data Expr = Variable Var
| Negation Expr
| Conjunction Expr Expr
| Disjunction Expr Expr
| Conditional Expr Expr
| Biconditional Expr Expr
deriving Eq
Working With SBV
Working with SBV requires that you follow the types and realize the Predicate is just a Symbolic SBool. After that step it is important that you investigate and discover Symbolic is a monad - yay, a monad!
Now that you you know you have a monad then anything in the haddock that is Symbolic should be trivial to combine to build any SAT you desire. For your problem you just need a simple interpreter over your AST that builds a Predicate.
Code Walk-Through
The code is all included in one continuous form below but I will step through the fun parts. The entry point is solveExpr which takes expressions and produces a SAT result:
solveExpr :: Expr -> IO AllSatResult
solveExpr e0 = allSat prd
The application of SBV's allSat to the predicate is sort of obvious. To build that predicate we need to declare an existential SBool for every variable in our expression. For now lets assume we have vs :: [String] where each string corresponds to one of the Var from the expression.
prd :: Predicate
prd = do
syms <- mapM exists vs
let env = M.fromList (zip vs syms)
interpret env e0
Notice how programming language fundamentals is sneaking in here. We now need an environment that maps the expressions variable names to the symbolic booleans used by SBV.
Next we interpret the expression to produce our Predicate. The interpret function uses the environment and just applies the SBV function that matches the intent of each constructor from hatt's Expr type.
interpret :: Env -> Expr -> Predicate
interpret env expr = do
let interp = interpret env
case expr of
Variable v -> return (envLookup v env)
Negation e -> bnot `fmap` interp e
Conjunction e1 e2 ->
do r1 <- interp e1
r2 <- interp e2
return (r1 &&& r2)
Disjunction e1 e2 ->
do r1 <- interp e1
r2 <- interp e2
return (r1 ||| r2)
Conditional e1 e2 -> error "And so on"
Biconditional e1 e2 -> error "And so on"
And that is it! The rest is just boiler-plate.
Complete Code
import Data.Logic.Propositional hiding (interpret)
import Data.SBV
import Text.Parsec.Error (ParseError)
import qualified Data.Map as M
import qualified Data.Set as Set
import Data.Foldable (foldMap)
import Control.Monad ((<=<))
testParse :: Either ParseError Expr
testParse = parseExpr "test source" "((X | ~Z) & (Y | ~Z))"
type Env = M.Map String SBool
envLookup :: Var -> Env -> SBool
envLookup (Var v) e = maybe (error $ "Var not found: " ++ show v) id
(M.lookup [v] e)
solveExpr :: Expr -> IO AllSatResult
solveExpr e0 = allSat go
where
vs :: [String]
vs = map (\(Var c) -> [c]) (variables e0)
go :: Predicate
go = do
syms <- mapM exists vs
let env = M.fromList (zip vs syms)
interpret env e0
interpret :: Env -> Expr -> Predicate
interpret env expr = do
let interp = interpret env
case expr of
Variable v -> return (envLookup v env)
Negation e -> bnot `fmap` interp e
Conjunction e1 e2 ->
do r1 <- interp e1
r2 <- interp e2
return (r1 &&& r2)
Disjunction e1 e2 ->
do r1 <- interp e1
r2 <- interp e2
return (r1 ||| r2)
Conditional e1 e2 -> error "And so on"
Biconditional e1 e2 -> error "And so on"
main :: IO ()
main = do
let expr = testParse
putStrLn $ "Solving expr: " ++ show expr
either (error . show) (print <=< solveExpr) expr
forSome_ is a member of the Provable class, so it seems it would suffice to define the instance Provable Expr. Almost all functions in SVB use Provable so this would allow you to use all of those natively Expr. First, we convert an Expr to a function which looks up variable values in a Vector. You could also use Data.Map.Map or something like that, but the environment is not changed once created and Vector gives constant time lookup:
import Data.Logic.Propositional
import Data.SBV.Bridge.CVC4
import qualified Data.Vector as V
import Control.Monad
toFunc :: Boolean a => Expr -> V.Vector a -> a
toFunc (Variable (Var x)) = \env -> env V.! (fromEnum x)
toFunc (Negation x) = \env -> bnot (toFunc x env)
toFunc (Conjunction a b) = \env -> toFunc a env &&& toFunc b env
toFunc (Disjunction a b) = \env -> toFunc a env ||| toFunc b env
toFunc (Conditional a b) = \env -> toFunc a env ==> toFunc b env
toFunc (Biconditional a b) = \env -> toFunc a env <=> toFunc b env
Provable essentially defines two functions: forAll_, forAll, forSome_, forSome. We have to generate all possible maps of variables to values and apply the function to the maps. Choosing how exactly to handle the results will be done by the Symbolic monad:
forAllExp_ :: Expr -> Symbolic SBool
forAllExp_ e = (m0 >>= f . V.accum (const id) (V.replicate (fromEnum maxV + 1) false)
where f = return . toFunc e
maxV = maximum $ map (\(Var x) -> x) (variables e)
m0 = mapM fresh (variables e)
Where fresh is a function which "quantifies" the given variable by associating it with all possible values.
fresh :: Var -> Symbolic (Int, SBool)
fresh (Var var) = forall >>= \a -> return (fromEnum var, a)
If you define one of these functions for each of the four functions you will have quite a lot of very repetitive code. So you can generalize the above as follows:
quantExp :: (String -> Symbolic SBool) -> Symbolic SBool -> [String] -> Expr -> Symbolic SBool
quantExp q q_ s e = m0 >>= f . V.accum (const id) (V.replicate (fromEnum maxV + 1) false)
where f = return . toFunc e
maxV = maximum $ map (\(Var x) -> x) (variables e)
(v0, v1) = splitAt (length s) (variables e)
m0 = zipWithM fresh (map q s) v0 >>= \r0 -> mapM (fresh q_) v1 >>= \r1 -> return (r0++r1)
fresh :: Symbolic SBool -> Var -> Symbolic (Int, SBool)
fresh q (Var var) = q >>= \a -> return (fromEnum var, a)
If it is confusing exactly what is happening, the Provable instance may suffice to explain:
instance Provable Expr where
forAll_ = quantExp forall forall_ []
forAll = quantExp forall forall_
forSome_ = quantExp exists exists_ []
forSome = quantExp exists exists_
Then your test case:
myPredicate :: Predicate
myPredicate = forSome_ $ \x y z -> ((x :: SBool) ||| (bnot z)) &&& (y ||| (bnot z))
myPredicate' :: Predicate
myPredicate' = forSome_ $ let Right a = parseExpr "test source" "((X | ~Z) & (Y | ~Z))" in a
testSat = allSat myPredicate >>= print
testSat' = allSat myPredicate >>= print

Conversion from lambda term to combinatorial term

Suppose there are some data types to express lambda and combinatorial terms:
data Lam α = Var α -- v
| Abs α (Lam α) -- λv . e1
| App (Lam α) (Lam α) -- e1 e2
deriving (Eq, Show)
infixl 0 :#
data SKI α = V α -- x
| SKI α :# SKI α -- e1 e2
| I -- I
| K -- K
| S -- S
deriving (Eq, Show)
There is also a function to get a list of lambda term's free variables:
fv ∷ Eq α ⇒ Lam α → [α]
fv (Var v) = [v]
fv (Abs x e) = filter (/= x) $ fv e
fv (App e1 e2) = fv e1 ++ fv e2
To convert lambda term to combinatorial term abstract elimination rules could be usefull:
convert ∷ Eq α ⇒ Lam α → SKI α
1) T[x] => x
convert (Var x) = V x
2) T[(E₁ E₂)] => (T[E₁] T[E₂])
convert (App e1 e2) = (convert e1) :# (convert e2)
3) T[λx.E] => (K T[E]) (if x does not occur free in E)
convert (Abs x e) | x `notElem` fv e = K :# (convert e)
4) T[λx.x] => I
convert (Abs x (Var y)) = if x == y then I else K :# V y
5) T[λx.λy.E] => T[λx.T[λy.E]] (if x occurs free in E)
convert (Abs x (Abs y e)) | x `elem` fv e = convert (Abs x (convert (Abs y e)))
6) T[λx.(E₁ E₂)] => (S T[λx.E₁] T[λx.E₂])
convert (Abs x (App y z)) = S :# (convert (Abs x y)) :# (convert (Abs x z))
convert _ = error ":["
This definition is not valid because of 5):
Couldn't match expected type `Lam α' with actual type `SKI α'
In the return type of a call of `convert'
In the second argument of `Abs', namely `(convert (Abs y e))'
In the first argument of `convert', namely
`(Abs x (convert (Abs y e)))'
So, what I have now is:
> convert $ Abs "x" $ Abs "y" $ App (Var "y") (Var "x")
*** Exception: :[
What I want is (hope I calculate it right):
> convert $ Abs "x" $ Abs "y" $ App (Var "y") (Var "x")
S :# (S (KS) (S (KK) I)) (S (KK) I)
Question:
If lambda term and combinatorial term have a different types of expression, how 5) could be formulated right?
Let's consider the equation T[λx.λy.E] => T[λx.T[λy.E]].
We know the result of T[λy.E] is an SKI expression. Since it has been produced by one of the cases 3, 4 or 6, it is either I or an application (:#).
Thus the outer T in T[λx.T[λy.E]] must be one of the cases 3 or 6. You can perform this case analysis in the code. I'm sorry but I don't have the time to write it out.
Here it's better to have a common data type for combinators and lambda expressions. Notice that your types already have significant overlap (Var, App), and it doesn't hurt to have combinators in lambda expressions.
The only possibility we want to eliminate is having lambda abstractions in combinator terms. We can forbid them using indexed types.
In the following code the type of a term is parameterised by the number of nested lambda abstractions in that term. The convert function returns Term Z a, where Z means zero, so there are no lambda abstractions in the returned term.
For more information about singleton types (which are used a bit here), see the paper Dependently Typed Programming with Singletons.
{-# LANGUAGE DataKinds, KindSignatures, TypeFamilies, GADTs, TypeOperators,
ScopedTypeVariables, MultiParamTypeClasses, FlexibleInstances #-}
data Nat = Z | Inc Nat
data SNat :: Nat -> * where
SZ :: SNat Z
SInc :: NatSingleton n => SNat n -> SNat (Inc n)
class NatSingleton (a :: Nat) where
sing :: SNat a
instance NatSingleton Z where sing = SZ
instance NatSingleton a => NatSingleton (Inc a) where sing = SInc sing
type family Max (a :: Nat) (b :: Nat) :: Nat
type instance Max Z a = a
type instance Max a Z = a
type instance Max (Inc a) (Inc b) = Inc (Max a b)
data Term (l :: Nat) a where
Var :: a -> Term Z a
Abs :: NatSingleton l => a -> Term l a -> Term (Inc l) a
App :: (NatSingleton l1, NatSingleton l2)
=> Term l1 a -> Term l2 a -> Term (Max l1 l2) a
I :: Term Z a
K :: Term Z a
S :: Term Z a
fv :: Eq a => Term l a -> [a]
fv (Var v) = [v]
fv (Abs x e) = filter (/= x) $ fv e
fv (App e1 e2) = fv e1 ++ fv e2
fv _ = []
eliminateLambda :: (Eq a, NatSingleton l) => Term (Inc l) a -> Term l a
eliminateLambda t =
case t of
Abs x t ->
case t of
Var y
| y == x -> I
| otherwise -> App K (Var y)
Abs {} -> Abs x $ eliminateLambda t
App a b -> S `App` (eliminateLambda $ Abs x a)
`App` (eliminateLambda $ Abs x b)
App a b -> eliminateLambdaApp a b
eliminateLambdaApp
:: forall a l1 l2 l .
(Eq a, Max l1 l2 ~ Inc l,
NatSingleton l1,
NatSingleton l2)
=> Term l1 a -> Term l2 a -> Term l a
eliminateLambdaApp a b =
case (sing :: SNat l1, sing :: SNat l2) of
(SInc _, SZ ) -> App (eliminateLambda a) b
(SZ , SInc _) -> App a (eliminateLambda b)
(SInc _, SInc _) -> App (eliminateLambda a) (eliminateLambda b)
convert :: forall a l . Eq a => NatSingleton l => Term l a -> Term Z a
convert t =
case sing :: SNat l of
SZ -> t
SInc _ -> convert $ eliminateLambda t
The key insight is that S, K and I are just constant Lam terms, in the same way that 1, 2 and 3 are constant Ints. It would be pretty easy to make rule 5 type-check by making an inverse to the 'convert' function:
nvert :: SKI a -> Lam a
nvert S = Abs "x" (Abs "y" (Abs "z" (App (App (Var "x") (Var "z")) (App (Var "y") (Var "z")))))
nvert K = Abs "x" (Abs "y" (Var "x"))
nvert I = Abs "x" (Var "x")
nvert (V x) = Var x
nvert (x :# y) = App (nvert x) (nvert y)
Now we can use 'nvert' to make rule 5 type-check:
convert (Abs x (Abs y e)) | x `elem` fv e = convert (Abs x (nvert (convert (Abs y e))))
We can see that the left and the right are identical (we'll ignore the guard), except that 'Abs y e' on the left is replaced by 'nvert (convert (Abs y e))' on the right. Since 'convert' and 'nvert' are each others' inverse, we can always replace any Lam 'x' with 'nvert (convert x)' and likewise we can always replace any SKI 'x' with 'convert (nvert x)', so this is a valid equation.
Unfortunately, while it's a valid equation it's not a useful function definition because it won't cause the computation to progress: we'll just convert 'Abs y e' back and forth forever!
To break this loop we can replace the call to 'nvert' with a 'reminder' that we should do it later. We do this by adding a new constructor to Lam:
data Lam a = Var a -- v
| Abs a (Lam a) -- \v . e1
| App (Lam a) (Lam a) -- e1 e2
| Com (SKI a) -- Reminder to COMe back later and nvert
deriving (Eq, Show)
Now rule 5 uses this reminder instead of 'nvert':
convert (Abs x (Abs y e)) | x `elem` fv e = convert (Abs x (Com (convert (Abs y e))))
Now we need to make good our promise to come back, by making a separate rule to replace reminders with actual calls to 'nvert', like this:
convert (Com c) = convert (nvert c)
Now we can finally break the loop: we know that 'convert (nvert c)' is always identical to 'c', so we can replace the above line with this:
convert (Com c) = c
Notice that our final definition of 'convert' doesn't actually use 'nvert' at all! It's still a handy function though, since other functions involving Lam can use it to handle the new 'Com' case.
You've probably noticed that I've actually named this constructor 'Com' because it's just a wrapped-up COMbinator, but I thought it would be more informative to take a slightly longer route than just saying "wrap up your SKIs in Lams" :)
If you're wondering why I called that function "nvert", see http://unapologetic.wordpress.com/2007/05/31/duality-terminology/ :)
Warbo is right, combinators are constant lambda terms, consequently the conversion function is
T[ ]:L -> C with L the set of lambda terms and C that of combinatory terms and with C ⊂ L .
So there is no typing problem for the rule T[λx.λy.E] => T[λx.T[λy.E]]
Here an implementation in Scala.

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