In any language design, the total time spent discussing a feature in this list is proportional to two raised to the power of its position.

• 0. Semantics
• 1. Syntax
• 2. Lexical syntax
• 3. Lexical syntax of comments
• 4. Semantics of records

I base the Weak Record Conjecture on the stable of proposed record semantics, which now includes (among others) Simple Overloaded Record Fields (SORF), Agda-derived Records (ADR), Frege-derived Records (FDR), Type-Punning Declared Overloaded Record Fields (TPDORF), Syntax Directed Name Resolution, Type Indexed Records and the less seriously proposed Homotopy Extensional Record Proposal (HERP) and Dependent Extensional Record Proposal (DERP).

There is an additional option implied but not stated in all of this, which is the option to "Leave Well Enough Alone" (LWEA?), since you can always Man Up and Learn Lenses (MUALL). Given that every record proposal I've seen thus far breaks polymorphic field updates to some degree, and lenses are going to be compatible with whatever mess folks settle on, even preserving the status quo, this is the path I've chosen to take.

Now, based on the fact that discussions of syntax have already started, and the intuition supplied by the ordering already present in Wadler's insightful law, I would also like to conjecture that perhaps an even stronger version of Wadler's law might be able to be stated, the "Strong Record Conjecture".

In any language design, the total time spent discussing a feature in this list is proportional to two raised to the power of its position.

• 0. Semantics
• 1. Syntax
• 2. Lexical syntax
• 3. Lexical syntax of comments
• 4. Semantics of records
• 5. Syntax of records
• 6. Lexical syntax of records

Under the Strong Record Conjecture, even in the unlikely event that universal accord could be reached on record semantics today — 164 days into this discussion — we'd still be due for at least another 3 years (328 + 656 days) of backlogged complaining over the syntax before anything gets done.

The evidence thus far is pretty strong that at least the Weak Record Conjecture holds — if anything the exponent is too small and may require further calibration, but we don't have much data yet on the Strong Record Conjecture. Consequently, and in the name of science, I plan to check in again on the record debate in 3 years. Hopefully by then we will have resolved the remaining semantic issues, and will have a better feel for the necessary time commitment required to resolve items 5 and 6.

A video of his talk is available over on his blog, and his presentation is remarkably clear, and would serve as a good preamble to the code I'm going to present below.

Andrej gave a related invited talk back at MSFP 2008 in Iceland, and afterwards over lunch I cornered him (with Dan Piponi) and explained how you could use parametricity to close over the side-effects of monads (or arrows, etc) but I think that trick was lost in the chaos of the weekend, so I've chosen to resurrect it here, and improve it to handle some of his more recent performance enhancements, and show that you don't need side-effects to speed up the search after all!

First, we'll need to import a few things:

 
{-# LANGUAGE RankNTypes #-}
 
import Data.Maybe (fromMaybe)
import Control.Applicative
import Data.IntMap (IntMap)
import qualified Data.IntMap as IntMap
import Control.Monad
import Control.Monad.Trans.Class
import Data.Functor.Identity
 

What are looking for is an implementation of Hilbert's epsilon.

This is a formal mechanism for eliminating existentials over some non-empty set X by defining a function

 
ε: (X -> Prop) -> X
 

such that if there exists an x in X such that p(X) holds then p(ε(p)) holds.

As noted by Andrej, we could reify this constructively as a function "epsilon :: (X -> Bool) -> X" for some X.

Now, for some sets, Hilbert's epsilon is really easy to define. If X is a finite set, you can just exhaustively enumerate all of the options returning a member of X such that the property holds if any of them do, otherwise since X is non-empty, just return one of the elements that you tested.

This would be a pretty boring article and I'd be back to eating Christmas dinner with my family if that was all there was to it. However, certain infinite spaces can also be searched.

Last year, Luke Palmer wrote a post on "Searchable Data Types" that might also serve as a good introduction. In that article he led off with the easiest infinite space to search, the lazy naturals, or the 'one point compactification of the naturals'. That is to say the natural numbers extended with infinity.

 
data LazyNat = Zero | Succ LazyNat
infinity :: LazyNat
infinity = Succ infinity
 

Now we can implement Palmer's epsilon (called lyingSearch in his article).

 
palmer :: (LazyNat -> Bool) -> LazyNat
palmer p
  | p Zero = Zero
  | otherwise = Succ $ palmer $ p . Succ
 

The trick to making this work is that we place a requirement that the predicate that you pass has to terminate in a bounded amount of time no matter what input you give it, and since we're working with the naturals extended with infinity, if no natural satisfies the predicate, we'll just keep returning a longer and longer chain of Succ's, effectively yielding infinity.

To check to see if the returned number satisfies the predicate you can always use p (palmer p). The predicate is required to terminate in finite time, even when given infinity, so this will yield a Bool and not bottom out unless the user supplied predicate takes an unbounded amount of time.

I posted a reply to Luke's article when it came up on reddit which included a Hinze-style generic implementation of his lyingSearch predicate, which you can see now is just Hilbert's epsilon for arbitrary recursive polynomial data types.

http://www.reddit.com/r/haskell/comments/e7nij/searchable_data_types/c15zs6l

Another space we can search is the Cantor space 2^N.

 
type Cantor = Integer -> Bool
 

With that we jump clear from countable infinity to uncountable infinity, but it can still be searched in finite time!

This is the space we'll be paying attention to for the rest of this article.

First we'll define how to "book a room in Hilbert's Hotel."

 
infixr 0 #
(#) :: Bool -> Cantor -> Cantor
(x # a) 0 = x
(x # a) i = a (i - 1)
 

Then this can be used to obtain the following implementation of Hilbert's epsilon for the Cantor space, attributed by Andrej to Ulrich Berger.

 
berger :: (Cantor -> Bool) -> Cantor
berger p =
  if ex $ \a -> p $ False # a
  then False # berger $ \a -> p $ False # a
  else True  # berger $ \a -> p $ True # a
  where ex q = q (berger q)
 

This version is particularly close in structure to the one for searching the LazyNats, but it is dreadfully slow!

It would be nice to be able to search the space faster and that is just what Martin Escardo's improved version does, through a more sophisticated divide and conquer technique.

 
escardo :: (Cantor -> Bool) -> Cantor
escardo p = go x l r where
  go x l r n =  case divMod n 2 of
    (0, 0) -> x
    (q, 1) -> l q
    (q, 0) -> r $ q-1
  x = ex $ \l -> ex $ \r -> p $ go True l r
  l = escardo $ \l -> ex $ \r -> p $ go x l r
  r = escardo $ \r -> p $ go x l r
  ex q = q (escardo q)
 

To proceed from here I'll need a State monad:

 
newtype S s a = S { runS :: s -> (a, s) }
 
instance Functor (S s) where
  fmap f (S m) = S $ \s -> case m s of
    (a, s') -> (f a, s')
 
instance Applicative (S s) where
  pure = return
  (< *>) = ap
 
instance Monad (S m) where
  return a = S $ \s -> (a, s)
  S m >>= k = S $ \s -> case m s of
    (a, s') -> runS (k a) s'
 

And now we've reached the point. From here, Andrej's pure code ends, and his side-effecting ocaml and custom programming language start. The first thing he does is compute the modulus of continuity by using a side-effect that writes to a reference cell which he very carefully ensures doesn't leak out of scope, so he doesn't have to concern himself with the proposition code editing the value of the reference.

 
let mu f a =
  let r = ref 0 in
  let b n = (r := max n ! r; a n) in
    ignore (f b);
    !r
 

To obtain the same effect we'll instead make a predicate using the state monad to model the single reference cell.

 
-- bad
modulus :: (Num b, Ord s) =>
  ((s -> S s a) -> S b c) -> (s -> a) -> b
 

We can mash b and s together, and try to make the ordering and number agree by claiming that it is instead Real and we'd get the slightly more reasonable looking type:

 
-- still bad
modulus :: Real a =>
  ((a -> S n b) -> S n c) -> (a -> b) -> a
 

In the imperative code, lexical scoping had ensured that no other code could edit the reference cell, but with this type we don't have that. The predicate is allowed to use arbitrary state actions to muck with the modulus of convergence even though the only thing that should be editing it is the wrapper beta that we placed around alpha.

But how can we ensure that the end user couldn't gain access to any of the additional functionality from the monad? Parametricity!

 
-- getting better
modulus :: Real a =>
  (forall f. Monad f => (a -> f b) -> f c) ->
  (a -> b) ->
  a
 

Here the only thing you are allowed to assume about f is that it forms a monad. This gives you access to return and >>=, but the predicate can't do anything interesting with them. All it can do is work with what is effectively the identity monad, since it knows no additional properties!

We can have mercy on the end user and give them a little bit more syntactic sugar, since it doesn't cost us anything to let them also have access to the Applicative instance.

 
-- good
modulus :: Real a =>
  (forall f. (Monad f, Applicative f) => (a -> f b) -> f c) ->
  (a -> b) ->
  a
 

With that we can show Andrej's version of the modulus of convergence calculation does not need side-effects!

>
> modulus (\a -> a 10 >>= a) (\n -> n * n)
100
 

Admittedly plumbing around the monadic values in our proposition is a bit inconvenient.

His next example was written in a custom ocaml-like programming language. For translating his effect type into Haskell using parametricity, we'll need a CPS'd state monad, so we can retry from the current continuation while we track a map of assigned values.

 
newtype K r s a = K { runK :: (a -> s -> r) -> s -> r }
 
instance Functor (K r s) where
  fmap = liftM
 
instance Applicative (K r s) where
  pure = return
  (< *>) = ap
 
instance Monad (K r s) where
  return a = K $ \k -> k a
  K m >>= f = K $ \k -> m $ \a -> runK (f a) k
 

For those of you who have been paying attention to my previous posts, K r s is just a Codensity monad!

 
neighborhood ::
  (forall f. (Applicative f, Monad f) => (Int -> f Bool) -> f Bool) ->
  IntMap Bool
neighborhood phi = snd $ runK (phi beta) (,) IntMap.empty where
  beta n = K $ \k s -> case IntMap.lookup n s of
    Just b -> k b s
    Nothing -> case k True (IntMap.insert n True s) of
      (False, _) -> k False (IntMap.insert n False s)
      r -> r
 

With that we can adapt the final version of Hilbert's epsilon for the Cantor space that Andrej provided to run in pure Haskell.

 
bauer ::
  (forall f. (Applicative f, Monad f) => (Int -> f Bool) -> f Bool) ->
  Cantor
bauer p = \n -> fromMaybe True $ IntMap.lookup n m where
  m = neighborhood p
 

With a little work you can implement a version of an exists and forAll predicate on top of that by running them through the identity monad.

 
exists ::
  (forall f. (Applicative f, Monad f) => (Int -> f Bool) -> f Bool) ->
  Bool
forAll ::
  (forall f. (Applicative f, Monad f) => (Int -> f Bool) -> f Bool) ->
  Bool
 

I've gone further in playing with this idea, using monad homomorphisms rather than simply relying on the canonical homomorphism from the identity monad. You can get the gist of it here:

https://gist.github.com/1518767

This permits the predicates themselves to embed some limited monadic side-effects, but then you get more extensional vs. intensional issues.

An obvious direction from here is to fiddle with a version of Martin Escardo's search monad that takes advantage of these techniques, but I'll leave the exploration of these ideas to the reader for now and go enjoy Christmas dinner.

Happy Holidays,
Edward Kmett

Reflecting Classes and Instances

Most of the implications we use on a day to day basis come from our class and instance declarations, but last time we only really dealt with constraint products.

For example given:

 
#if 0
class Eq a => Ord a
instance Eq a => Eq [a]
#endif
 

we could provide the following witnesses

 
ordEq :: Ord a :- Eq a
ordEq = Sub Dict
 
eqList :: Eq a :- Eq [a]
eqList = Sub Dict
 

But this would require a lot of names and become remarkably tedious.

So lets define classes to reflect the entailment provided by class definitions and instance declarations and then use them to reflect themselves.

 
class Class b h | h -> b where
  cls :: h :- b
 
infixr 9 :=>
class b :=> h | h -> b where
  ins :: b :- h
 
instance Class () (Class b a) where cls = Sub Dict
instance Class () (b :=> a) where cls = Sub Dict
 

Now we can reflect classes and instances as instances of Class and (:=>) respectively with:

 
-- class Eq a => Ord a where ...
instance Class (Eq a) (Ord a) where cls = Sub Dict
-- instance Eq a => Eq [a] where ...
instance Eq a :=> Eq [a] where ins = Sub Dict
 

That said, instances of Class and Instance should never require a context themselves, because the modules that the class and instance declarations live in can't taken one, so we can define the following instances which bootstrap the instances of (:=>) for Class and (:=>) once and for all.

 
#ifdef UNDECIDABLE
instance Class b a => () :=> Class b a where ins = Sub Dict
instance (b :=> a) => () :=> b :=> a where ins = Sub Dict
#endif
 

These two instances are both decidable, and following a recent bug fix, the current version of GHC HEAD supports them, but my local version isn't that recent, hence the #ifdef.

We can also give admissable-if-not-ever-stated instances of Class and (:=>) for () as well.

 
instance Class () () where cls = Sub Dict
instance () :=> () where ins = Sub Dict
 

Reflecting the Prelude

So now that we've written a handful of instances, lets take the plunge and just reflect the entire Prelude, and (most of) the instances for the other modules we've loaded.

 
instance Class () (Eq a) where cls = Sub Dict
instance () :=> Eq () where ins = Sub Dict
instance () :=> Eq Int where ins = Sub Dict
instance () :=> Eq Bool where ins = Sub Dict
instance () :=> Eq Integer where ins = Sub Dict
instance () :=> Eq Float where ins = Sub Dict
instance () :=> Eq Double where ins = Sub Dict
instance Eq a :=> Eq [a] where ins = Sub Dict
instance Eq a :=> Eq (Maybe a) where ins = Sub Dict
instance Eq a :=> Eq (Complex a) where ins = Sub Dict
instance Eq a :=> Eq (Ratio a) where ins = Sub Dict
instance (Eq a, Eq b) :=> Eq (a, b) where ins = Sub Dict
instance (Eq a, Eq b) :=> Eq (Either a b) where ins = Sub Dict
instance () :=> Eq (Dict a) where ins = Sub Dict
instance () :=> Eq (a :- b) where ins = Sub Dict
 

 
instance Class (Eq a) (Ord a) where cls = Sub Dict
instance () :=> Ord () where ins = Sub Dict
instance () :=> Ord Bool where ins = Sub Dict
instance () :=> Ord Int where ins = Sub Dict
instance ():=> Ord Integer where ins = Sub Dict
instance () :=> Ord Float where ins = Sub Dict
instance ():=> Ord Double where ins = Sub Dict
instance () :=> Ord Char where ins = Sub Dict
instance Ord a :=> Ord (Maybe a) where ins = Sub Dict
instance Ord a :=> Ord [a] where ins = Sub Dict
instance (Ord a, Ord b) :=> Ord (a, b) where ins = Sub Dict
instance (Ord a, Ord b) :=> Ord (Either a b) where ins = Sub Dict
instance Integral a :=> Ord (Ratio a) where ins = Sub Dict
instance () :=> Ord (Dict a) where ins = Sub Dict
instance () :=> Ord (a :- b) where ins = Sub Dict
 

 
instance Class () (Show a) where cls = Sub Dict
instance () :=> Show () where ins = Sub Dict
instance () :=> Show Bool where ins = Sub Dict
instance () :=> Show Ordering where ins = Sub Dict
instance () :=> Show Char where ins = Sub Dict
instance Show a :=> Show (Complex a) where ins = Sub Dict
instance Show a :=> Show [a] where ins = Sub Dict
instance Show a :=> Show (Maybe a) where ins = Sub Dict
instance (Show a, Show b) :=> Show (a, b) where ins = Sub Dict
instance (Show a, Show b) :=> Show (Either a b) where ins = Sub Dict
instance (Integral a, Show a) :=> Show (Ratio a) where ins = Sub Dict
instance () :=> Show (Dict a) where ins = Sub Dict
instance () :=> Show (a :- b) where ins = Sub Dict
 

 
instance Class () (Read a) where cls = Sub Dict
instance () :=> Read () where ins = Sub Dict
instance () :=> Read Bool where ins = Sub Dict
instance () :=> Read Ordering where ins = Sub Dict
instance () :=> Read Char where ins = Sub Dict
instance Read a :=> Read (Complex a) where ins = Sub Dict
instance Read a :=> Read [a] where ins = Sub Dict
instance Read a :=> Read (Maybe a) where ins = Sub Dict
instance (Read a, Read b) :=> Read (a, b) where ins = Sub Dict
instance (Read a, Read b) :=> Read (Either a b) where ins = Sub Dict
instance (Integral a, Read a) :=> Read (Ratio a) where ins = Sub Dict
 

 
instance Class () (Enum a) where cls = Sub Dict
instance () :=> Enum () where ins = Sub Dict
instance () :=> Enum Bool where ins = Sub Dict
instance () :=> Enum Ordering where ins = Sub Dict
instance () :=> Enum Char where ins = Sub Dict
instance () :=> Enum Int where ins = Sub Dict
instance () :=> Enum Integer where ins = Sub Dict
instance () :=> Enum Float where ins = Sub Dict
instance () :=> Enum Double where ins = Sub Dict
instance Integral a :=> Enum (Ratio a) where ins = Sub Dict
 

 
instance Class () (Bounded a) where cls = Sub Dict
instance () :=> Bounded () where ins = Sub Dict
instance () :=> Bounded Ordering where ins = Sub Dict
instance () :=> Bounded Bool where ins = Sub Dict
instance () :=> Bounded Int where ins = Sub Dict
instance () :=> Bounded Char where ins = Sub Dict
instance (Bounded a, Bounded b) :=> Bounded (a,b) where ins = Sub Dict
 

 
instance Class () (Num a) where cls = Sub Dict
instance () :=> Num Int where ins = Sub Dict
instance () :=> Num Integer where ins = Sub Dict
instance () :=> Num Float where ins = Sub Dict
instance () :=> Num Double where ins = Sub Dict
instance RealFloat a :=> Num (Complex a) where ins = Sub Dict
instance Integral a :=> Num (Ratio a) where ins = Sub Dict
 

 
instance Class (Num a, Ord a) (Real a) where cls = Sub Dict
instance () :=> Real Int where ins = Sub Dict
instance () :=> Real Integer where ins = Sub Dict
instance () :=> Real Float where ins = Sub Dict
instance () :=> Real Double where ins = Sub Dict
instance Integral a :=> Real (Ratio a) where ins = Sub Dict
 

 
instance Class (Real a, Enum a) (Integral a) where cls = Sub Dict
instance () :=> Integral Int where ins = Sub Dict
instance () :=> Integral Integer where ins = Sub Dict
 

 
instance Class (Num a) (Fractional a) where cls = Sub Dict
instance () :=> Fractional Float where ins = Sub Dict
instance () :=> Fractional Double where ins = Sub Dict
instance RealFloat a :=> Fractional (Complex a) where ins = Sub Dict
instance Integral a :=> Fractional (Ratio a) where ins = Sub Dict
 

 
instance Class (Fractional a) (Floating a) where cls = Sub Dict
instance () :=> Floating Float where ins = Sub Dict
instance () :=> Floating Double where ins = Sub Dict
instance RealFloat a :=> Floating (Complex a) where ins = Sub Dict
 

 
instance Class (Real a, Fractional a) (RealFrac a) where cls = Sub Dict
instance () :=> RealFrac Float where ins = Sub Dict
instance () :=> RealFrac Double where ins = Sub Dict
instance Integral a :=> RealFrac (Ratio a) where ins = Sub Dict
 

 
instance Class (RealFrac a, Floating a) (RealFloat a) where cls = Sub Dict
instance () :=> RealFloat Float where ins = Sub Dict
instance () :=> RealFloat Double where ins = Sub Dict
 

 
instance Class () (Monoid a) where cls = Sub Dict
instance () :=> Monoid () where ins = Sub Dict
instance () :=> Monoid Ordering where ins = Sub Dict
instance () :=> Monoid [a] where ins = Sub Dict
instance Monoid a :=> Monoid (Maybe a) where ins = Sub Dict
instance (Monoid a, Monoid b) :=> Monoid (a, b) where ins = Sub Dict
 

 
instance Class () (Functor f) where cls = Sub Dict
instance () :=> Functor [] where ins = Sub Dict
instance () :=> Functor Maybe where ins = Sub Dict
instance () :=> Functor (Either a) where ins = Sub Dict
instance () :=> Functor ((->) a) where ins = Sub Dict
instance () :=> Functor ((,) a) where ins = Sub Dict
instance () :=> Functor IO where ins = Sub Dict
 

 
instance Class (Functor f) (Applicative f) where cls = Sub Dict
instance () :=> Applicative [] where ins = Sub Dict
instance () :=> Applicative Maybe where ins = Sub Dict
instance () :=> Applicative (Either a) where ins = Sub Dict
instance () :=> Applicative ((->)a) where ins = Sub Dict
instance () :=> Applicative IO where ins = Sub Dict
instance Monoid a :=> Applicative ((,)a) where ins = Sub Dict
 

 
instance Class (Applicative f) (Alternative f) where cls = Sub Dict
instance () :=> Alternative [] where ins = Sub Dict
instance () :=> Alternative Maybe where ins = Sub Dict
 

 
instance Class () (Monad f) where cls = Sub Dict
instance () :=> Monad [] where ins = Sub Dict
instance () :=> Monad ((->) a) where ins = Sub Dict
instance () :=> Monad (Either a) where ins = Sub Dict
instance () :=> Monad IO where ins = Sub Dict
 

 
instance Class (Monad f) (MonadPlus f) where cls = Sub Dict
instance () :=> MonadPlus [] where ins = Sub Dict
instance () :=> MonadPlus Maybe where ins = Sub Dict
 

Of course, the structure of these definitions is extremely formulaic, so when template-haskell builds against HEAD again, they should be able to be generated automatically using splicing and reify, which would reduce this from a wall of text to a handful of lines with better coverage!

An alternative using Default Signatures and Type Families

Many of the above definitions could have been streamlined by using default definitions. However, MPTCs do not currently support default signatures. We can however, define Class and (:=>) using type families rather than functional dependencies. This enables us to use defaulting, whenever the superclass or context was ().

 
#if 0
class Class h where
  type Sup h :: Constraint
  type Sup h = ()
  cls :: h :- Sup h
  default cls :: h :- ()
  cls = Sub Dict
 
class Instance h where
  type Ctx h :: Constraint
  type Ctx h = ()
  ins :: Ctx h :- h
  default ins :: h => Ctx h :- h
  ins = Sub Dict
 
instance Class (Class a)
instance Class (Instance a)
 
#ifdef UNDECIDABLE
instance Class a => Instance (Class a)
instance Instance a => Instance (Instance a)
#endif
 
instance Class ()
instance Instance ()
#endif
 

This seems at first to be a promising approach. Many instances are quite small:

 
#if 0
instance Class (Eq a)
instance Instance (Eq ())
instance Instance (Eq Int)
instance Instance (Eq Bool)
instance Instance (Eq Integer)
instance Instance (Eq Float)
instance Instance (Eq Double)
#endif
 

But those that aren't are considerably more verbose and are much harder to read off than the definitions using the MPTC based Class and (:=>).

 
#if 0
instance Instance (Eq [a]) where
  type Ctx (Eq [a]) = Eq a
  ins = Sub Dict
instance Instance (Eq (Maybe a)) where
  type Ctx (Eq (Maybe a)) = Eq a
  ins = Sub Dict
instance Instance (Eq (Complex a)) where
  type Ctx (Eq (Complex a)) = Eq a
  ins = Sub Dict
instance Instance (Eq (Ratio a)) where
  type Ctx (Eq (Ratio a)) = Eq a
  ins = Sub Dict
instance Instance (Eq (a, b)) where
  type Ctx (Eq (a,b)) = (Eq a, Eq b)
  ins = Sub Dict
instance Instance (Eq (Either a b)) where
  type Ctx (Eq (Either a b)) = (Eq a, Eq b)
  ins = Sub Dict
#endif
 

Having tested both approaches, the type family approach led to a ~10% larger file size, and was harder to read, so I remained with MPTCs even though it meant repeating "where ins = Sub Dict" over and over.

In a perfect world, we'd gain the ability to use default signatures with multiparameter type classes, and the result would be considerably shorter and easier to read!

Fake Superclasses

Now, that we have all this machinery, it'd be nice to get something useful out of it. Even if we could derive it by other means, it'd let us know we weren't completely wasting our time.

Let's define a rather horrid helper, which we'll only use where a and b are the same constraint being applied to a newtype wrapper of the same type, so we can rely on the fact that the dictionaries have the same representation.

 
evil :: a :- b
evil = unsafeCoerce refl
 

We often bemoan the fact that we can't use Applicative sugar given just a Monad, since Applicative wasn't made a superclass of Monad due to the inability of the Haskell 98 report to foresee the future invention of Applicative.

There are rather verbose options to get Applicative sugar for your Monad, or to pass it to something that expects an Applicative. For instance you can use WrappedMonad from Applicative. We reflect the relevant instance here.

 
instance Monad m :=> Applicative (WrappedMonad m) where ins = Sub Dict
 

Using that instance and the combinators defined previously, we can obtain the following

 
applicative :: forall m a. Monad m => (Applicative m => m a) -> m a
applicative m =
  m \\ trans (evil :: Applicative (WrappedMonad m) :- Applicative m) ins
 

Here ins is instantiated to the instance of (:=>) above, so we use trans to compose ins :: Monad m :- Applicative (WrappedMonad m) with evil :: Applicative (WrappedMonad m) :- Applicative m to obtain an entailment of type Monad m :- Applicative m in local scope, and then apply that transformation to discharge the Applicative obligation on m.

Now, we can use this to write definitions. [Note: Frustratingly, my blog software inserts spaces after <'s in code]

 
(< &>) :: Monad m => m a -> m b -> m (a, b)
m < &> n = applicative $ (,) < $> m < *> n
 

Which compares rather favorably to the more correct

 
(< &>) :: Monad m => m a -> m b -> m (a, b)
m < &> n = unwrapMonad $ (,) < $> WrapMonad m < *> WrapMonad n
 

especially considering you still have access to any other instances on m you might want to bring into scope without having to use deriving to lift them onto the newtype!

Similarly you can borrow < |> and empty locally for use by your MonadPlus with:

 
instance MonadPlus m :=> Alternative (WrappedMonad m) where ins = Sub Dict
 
alternative :: forall m a. MonadPlus m => (Alternative m => m a) -> m a
alternative m =
  m \\ trans (evil :: Alternative (WrappedMonad m) :- Alternative m) ins
 

The correctness of this of course relies upon the convention that any Applicative and Alternative your Monad may have should agree with its Monad instance, so even if you use Alternative or Applicative in a context where the actual Applicative or Alternative instance for your particular type m is in scope, it shouldn't matter beyond a little bit of efficiency which instance the compiler picks to discharge the Applicative or Alternative obligation.

Note: It isn't that the Constraint kind is invalid, but rather that using unsafeCoerce judiciously we can bring into scope instances that don't exist for a given type by substituting those from a different type which have the right representation.

[Source]

Constraint Kinds adds a new kind Constraint, such that Eq :: * -> Constraint, Monad :: (* -> *) -> Constraint, but since it is a kind, we can make type families for constraints, and even parameterize constraints on constraints.

So, let's play with them and see what we can come up with!

A Few Extensions

First, we'll need a few language features:

 
{-# LANGUAGE
  CPP,
  ScopedTypeVariables,
  FlexibleInstances,
  FlexibleContexts,
  ConstraintKinds,
  KindSignatures,
  TypeOperators,
  FunctionalDependencies,
  Rank2Types,
  StandaloneDeriving,
  GADTs
  #-}
 

Because of the particular version of GHC I'm using I'll also need

 
{-# LANGUAGE UndecidableInstances #-}
#define UNDECIDABLE
 

but this bug has been fixed in the current version of GHC Head. I'll be explicit about any instances that need UndecidableInstances by surrounding them in an #ifdef UNDECIDABLE block.

Explicit Dictionaries

So with that out of the way, let's import some definitions

 
import Control.Monad
import Control.Monad.Instances
import Control.Applicative
import Data.Monoid
import Data.Complex
import Data.Ratio
import Unsafe.Coerce
 

and make one of our own that shows what we get out of making Constraints into a kind we can manipulate like any other.

 
data Dict a where
  Dict :: a => Dict a
 

Previously, we coud make a Dict like data type for any one particular class constraint that we wanted to capture, but now we can write this type once and for all. The act of pattern matching on the Dict constructor will bring the constraint 'a' into scope.

Of course, in the absence of incoherent and overlapping instances there is at most one dictionary of a given type, so we could make instances, like we can for any other data type, but standalone deriving is smart enough to figure these out for me. (Thanks copumpkin!)

 
deriving instance Eq (Dict a)
deriving instance Ord (Dict a)
deriving instance Show (Dict a)
 

If we're willing to turn on UndecidableInstances to enable the polymorphic constraint we can even add:

 
#ifdef UNDECIDABLE
deriving instance a => Read (Dict a)
instance a => Monoid (Dict a) where
  mappend Dict Dict = Dict
  mempty = Dict
#endif
 

and similar polymorphically constrained instances for Enum, Bounded, etc.

Entailment

For that we'll need a notion of entailment.

 
infixr 9 :-
newtype a :- b = Sub (a => Dict b)
 
instance Eq (a :- b) where
  _ == _ = True
 
instance Ord (a :- b) where
  compare _ _ = EQ
 
instance Show (a :- b) where
  showsPrec d _ = showParen (d > 10) $
    showString "Sub Dict"
 

Here we're saying that Sub takes one argument, which is a computation that when implicitly given a constraint of type a, can give me back a dictionary for the type b. Moreover, as a newtype it adds no overhead that isn't aleady present in manipulating terms of type (a => Dict b) directly.

The simplest thing we can define with this is that entailment is reflexive.

 
refl :: a :- a
refl = Sub Dict
 

Max has already written up a nice restricted monad example using these, but what I want to play with today is the category of substitutability of constraints, but there are a few observations I need to make, first.

ConstraintKinds overloads () and (a,b) to represent the trivial constraint and the product of two constraints respectively.

The latter is done with a bit of a hack, which we'll talk about in a minute, but we can use the former as a terminal object for our category of entailments.

 
weaken1 :: (a, b) :- a
weaken1 = Sub Dict
 
weaken2 :: (a, b) :- b
weaken2 = Sub Dict
 

Constraints are idempotent, so we can duplicate one, perhaps as a prelude to transforming one of them into something else.

 
contract :: a :- (a, a)
contract = Sub Dict
 

But to do much more complicated, we're going to need a notion of substitution, letting us use our entailment relation to satisfy obligations.

 
infixl 1 \\ -- required comment
(\\) :: a => (b => r) -> (a :- b) -> r
r \\ Sub Dict = r
 

The type says that given that a constraint a can be satisfied, a computation that needs a constraint of type b to be satisfied in order to obtain a result, and the fact that a entails b, we can compute the result.

The constraint a is satisfied by the type signature, and the fact that we get quietly passed whatever dictionary is needed. Pattern matching on Sub brings into scope a computation of type (a => Dict b), and we are able to discharge the a obligation, using the dictionary we were passed, Pattern matching on Dict forces that computation to happen and brings b into scope, allowing us to meet the obligation of the computation of r. All of this happens for us behind the scenes just by pattern matching.

So what can we do with this?

We can use \\ to compose constraints.

 
trans :: (b :- c) -> (a :- b) -> a :- c
trans f g = Sub $ Dict \\ f \\ g
 

In fact, the way the dictionaries get plumbed around inside the argument to Sub is rather nice, because we can give that same definition different type signatures, letting us make (,) more product-like, giving us the canonical product morphism to go with the weakenings/projections we defined above.

 
(&&&) :: (a :- b) -> (a :- c) -> a :- (b, c)
f &&& g = Sub $ Dict \\ f \\ g
 

And since we're using it as a product, we can make it act like a bifunctor also using the same definition.

 
(***) :: (a :- b) -> (c :- d) -> (a, c) :- (b, d)
f *** g = Sub $ Dict \\ f \\ g
 

Limited Sub-Superkinding?

Ideally we'd be able to capture something like that bifunctoriality using a type like

 
#if 0
class BifunctorS (p :: Constraint -> Constraint -> Constraint) where
  bimapS :: (a :- b) -> (c :- d) -> p a c :- p b d
#endif
 

In an even more ideal world, it would be enriched using something like

 
#ifdef POLYMORPHIC_KINDS
class Category (k :: x -> x -> *) where
  id :: k a a
  (.) :: k b c -> k a b -> k a c
instance Category (:-) where
  id = refl
  (.) = trans
#endif
 

where x is a kind variable, then we could obtain a more baroque and admittedly far less thought-out bifunctor class like:

 
#if 0
class Bifunctor (p :: x -> y -> z) where
  type Left p :: x -> x -> *
  type Left p = (->)
  type Right p :: y -> y -> *
  type Right p = (->)
  type Cod p :: z -> z -> *
  type Cod p = (->)
  bimap :: Left p a b -> Right p c d -> Cod p (p a c) (p b d)
#endif
 

Or even more more ideally, you could use the fact that we can directly define product categories!

Since they are talking about kind-indexing for classes and type families, we could have separate bifunctors for (,) for both kinds * and Constraint.

The current constraint kind code uses a hack to let (a,b) be used as a type inhabiting * and as the syntax for constraints. This hack is limited however. It only works when the type (,) is fully applied to its arguments. Otherwise you'd wind up with the fact that the type (,) needs to have both of these kinds:

 
-- (,) :: Constraint -> Constraint -> Constraint and
-- (,) :: * -> * -> *
 

What is currently done is that the kind magically switches for () and (,) in certain circumstances. GHC already had some support for this because it parses (Foo a, Bar b) as a type in (Foo a, Bar b) => Baz a b before transforming it into a bunch of constraints.

Since we already have a notion of sub-kinding at the kind level, we could solve this for () by making up a new kind, say, ??? which is the subkind of both * and Constraint, but this would break the nice join lattice properties of the current system.

[Edit: in the initial draft, I had said superkind]

 
--    ?
--   / \
-- (#)  ??
--     /  \
--    #    *  Constraint
--          \ /
--          ???
 

But this doesn't address the kind of (,) above. With the new polymorphic kinds that Brent Yorgey and company have been working on and a limited notion of sub-superkinding, this could be resolved by making a new super-kind @ that is the super-kind of both * and Constraint, and which is a sub-superkind of the usual unnamed Box superkind.

 
-- Box
--  |
--  @
 

Then we can have:

 
-- (,) :: forall (k :: @). k -> k -> k
-- () :: forall (k :: @). k
 

and kind checking/inference will do the right thing about keeping the kind ambiguous for types like (,) () :: forall (k :: @). k

This would get rid of the hack and let me make a proper bifunctor for (,) in the category of entailments.

The version of GHC head I'm working with right now doesn't support polymorphic kinds, so I've only been playing with these in a toy type checker, but I'm really looking forward to being able to have product categories!

Stay Tuned

Next, we'll go over how to reflect the class and instance declarations so we can derive entailment of a superclass for a class, and the entailment of instances.

[Source]

Everyone is welcome -- you do not have to be a Haskell guru to attend! Helping hack on someone else's project could be a great way to increase your Haskell skills.

If you plan on coming, please officially register, even if you already put your name on the wiki. Registration, travel, some information about lodging and many other details can now be found on the Hac Boston wiki.

We have confirmed space for about 30 people, so please register early! Beyond that we'll have to either seek additional space or close registration.

We're also looking for a few people interested in giving short (15-20 min.) talks, probably on Saturday afternoon. Anything of interest to the Haskell community is fair game---a project you've been working on, a paper, a quick tutorial. If you'd like to give a talk, add it on the wiki.

We look forward to seeing you at MIT!

This time I intend to implement packrat directly on top of Parsec 3.

One of the main topics of discussion when it comes to packrat parsing since Bryan Ford's initial release of Pappy has been the fact that in general you shouldn't use packrat to memoize every rule, and that instead you should apply Amdahl's law to look for the cases where the lookup time is paid back in terms of repetitive evaluation, computation time and the hit rate. This is great news for us, since, we only want to memoize a handful of expensive combinators.

First, we'll need to import enough of Parsec to do something interesting.

 
{-# LANGUAGE RecordWildCards, ViewPatterns, FlexibleInstances, MultiParamTypeClasses #-}
 
import Text.Parsec
import qualified Text.Parsec.Token as T
import Text.Parsec.Token
    (GenLanguageDef(..), GenTokenParser(TokenParser))
import Text.Parsec.Pos (initialPos, updatePosChar)
import Data.Functor.Identity (Identity(..))
import Control.Applicative hiding ((< |>))
import Control.Monad.Fix (fix)
 

Then as before, we'll define PEG-style backtracking:

 
(< />) :: Monad m => ParsecT s u m a -> ParsecT s u m a ->
    ParsecT s u m a
p < /> q = try p < |> q
infixl 3 < />
 

Now we need an analogue to our Result type from last time, which recalled whether or not we had consumed input, and what the current cursor location is. Fortunately, we can recycle the definitions from Parsec to this end.

 
type Result d a = Consumed (Reply d () a)
 

We'll define a combinator to build a parser directly from a field accessor. Last time, this was just the use of the "Rat" constructor. Now it is a bit trickier, because we need to turn Consumed (Reply d () a) into m (Consumed (m (Reply d u a))) by wrapping it in the appropriate monad, and giving the user back his state unmolested.

 
rat :: Monad m => (d -> Result d a) -> ParsecT d u m a
rat f   = mkPT $ \s0 -> return $
    return . patch s0 < $> f (stateInput s0) where
  patch (State _ _ u) (Ok a (State s p _) err) = Ok a (State s p u) err
  patch _             (Error e)                = Error e
 

Last time we could go from a parser to a result just by applying the user stream type, but with parsec we also have to supply their notion of a position. This leads to the following combinator. By running in the Identity monad with no user state it should be obvious that we've duplicated the functionality of the previous 'Rat' parser (with the addition of a source position).

 
womp :: d -> SourcePos -> ParsecT d () Identity a -> Result d a
womp d pos p = fmap runIdentity . runIdentity $
    runParsecT p (State d pos ())
 

The combinator is so named because we needed a big space-rat rather than a little pack-rat to keep with the theme.

It's not impossible. I used to bullseye womp rats in my T-16 back home, they're not much bigger than two meters.

Now we'll write a bit of annoyingly verbose boilerplate to convince Parsec that we really want a LanguageDef for some monad other than Identity. (As an aside, why Text.Parsec.Language doesn't contain GenLanguageDefs that are parametric in their choice of Monad is beyond me.)

 
myLanguageDef :: Monad m => T.GenLanguageDef D u m
myLanguageDef = T.LanguageDef
  { commentStart    = "{-"
  , commentEnd      = "-}"
  , commentLine     = "--"
  , nestedComments  = True
  , identStart      = letter < |> char '_'
  , identLetter     = alphaNum < |> oneOf "_'"
  , opStart         = opLetter myLanguageDef
  , opLetter        = oneOf ":!#$%&*+./< =>?@\\^|-~"
  , reservedOpNames = []
  , reservedNames   = []
  , caseSensitive   = True
  }
 

As a shameless plug, trifecta offers a particularly nice solution to this problem, breaking up the monolithic Token type into separate concerns and letting you layer parser transformers that enrich the parser to deal with things like Haskell-style layout, literate comments, parsing comments in whitespace, etc.

And as one last bit of boilerplate, we'll abuse RecordWildcards once again to avoid the usual 20 lines of boilerplate that are expected of us, so we can get access to parsec's token parsers.

 
TokenParser {..} = T.makeTokenParser myLanguageDef
 

Now we're ready to define our incredibly straightforward stream type:

 
data D = D
  { _add        :: Result D Integer
  , _mult       :: Result D Integer
  , _primary    :: Result D Integer
  , _dec        :: Result D Integer
  , _uncons     :: Maybe (Char, D)
  }
 
instance Monad m => Stream D m Char where
  uncons = return . _uncons
 

And using the general purpose rat combinator from earlier, we can write some memoized parsers:

 
add, mult, primary, dec :: Parsec D u Integer
add     = rat _add
mult    = rat _mult
primary = rat _primary
dec     = rat _dec
 

And finally, we write the code to tie the knot and build the stream:

 
parse :: SourceName -> String -> D
parse n = go (initialPos n) where
  go p s = fix $ \d -> let
    (womp d p -> _add) =
            (+) < $> mult < * reservedOp "+" <*> add
        < /> mult < ?> "summand"
    (womp d p -> _mult) =
            (*) < $> primary < * reservedOp "*" <*> mult
        < /> primary < ?> "factor"
    (womp d p -> _primary) =
            parens add
        < /> dec < ?> "number"
    (womp d p -> _dec) = natural
    _uncons = case s of
      (x:xs) -> Just (x, go (updatePosChar p x) xs)
      []     -> Nothing
    in D { .. }
 
runD :: Parsec D u a -> u -> SourceName -> String -> Either ParseError a
runD p u fn s = runParser p u fn (prep fn s)
 

and finally, let it rip:

 
eval :: String -> Integer
eval s = either (error . show) id $
    runD (whiteSpace *> add < * eof) () "-" s
 

While this approach tends to encourage memoizing fewer combinators than libraries such as frisby, this is exactly what current research suggests you probably should do with packrat parsing!

The other purported advantage of packrat parsers is that they can deal with left recursion in the grammar. However, that is not the case, hidden left recursion in the presence of the algorithm used in the scala parsing combinator libraries leads to incorrect non-left-most parses as shown by Tratt.

I leave it as an exercise for the reader to extend this material with the parsec+iteratees approach from my original talk on trifecta to get packrat parsing of streaming input. Either that or you can wait until it is integrated into trifecta.

You can download the source to this (without the spurious spaces inserted by wordpress) here.

If I can find the time, I hope to spend some time addressing Scott and Johnstone's GLL parsers, which actually achieve the O(n^3) worst case bounds touted for Tomita's GLR algorithm (which is actually O(n^4) as it was originally defined despite the author's claims), and how to encode them in Haskell with an eye towards building a memoizing parser combinator library that can parse LL(1) fragments in O(1), deal with arbitrary context-free grammars in O(n^3), and degrade reasonably gracefully in the presence of context-sensitivity, while supporting hidden left recursion as long as such recursion passes through at least one memoized rule. This is important because CFGs are closed under extensions to the grammar, which is a nice property to have if you want to have a language where you can add new statement types easily without concerning yourself overmuch with the order in which you insert the rules or load the different extensions.

You never heard of the Millenium Falcon? It's the ship that made the Kessel Run in 12 parsecs.

I've been working on a parser combinator library called trifecta, and so I decided I'd share some thoughts on parsing.

Packrat parsing (as provided by frisby, pappy, rats! and the Scala parsing combinators) and more traditional recursive descent parsers (like Parsec) are often held up as somehow different.

Today I'll show that you can add monadic parsing to a packrat parser, sacrificing asymptotic guarantees in exchange for the convenient context sensitivity, and conversely how you can easily add packrat parsing to a traditional monadic parser combinator library.

To keep this post self-contained, I'm going to start by defining a small packrat parsing library by hand, which acts rather like parsec in its backtracking behavior. First, some imports:

 
{-# LANGUAGE RecordWildCards, ViewPatterns, DeriveFunctor #-}
import Control.Applicative
import Control.Monad (MonadPlus(..), guard)
import Control.Monad.Fix (fix)
import Data.Char (isDigit, digitToInt, isSpace)
 

Second, we'll define a bog simple parser, which consumes an input stream of type d, yielding a possible answer and telling us whether or not it has actually consumed any input as it went.

 
newtype Rat d a = Rat { runRat :: d -> Result d a }
  deriving Functor
 
data Result d a
  = Pure a             -- didn't consume anything, can backtrack
  | Commit d a      -- consumed input
  | Fail String Bool -- failed, flagged if consumed
  deriving Functor
 

Now, we can finally implement some type classes:

 
instance Applicative (Rat d) where
  pure a = Rat $ \ _ -> Pure a
  Rat mf < *> Rat ma = Rat $ \ d -> case mf d of
    Pure f      -> fmap f (ma d)
    Fail s c    -> Fail s c
    Commit d' f -> case ma d' of
      Pure a       -> Commit d' (f a)
      Fail s _     -> Fail s True
      Commit d'' a -> Commit d'' (f a)
 

including an instance of Alternative that behaves like parsec, only backtracking on failure if no input was unconsumed.

 
instance Alternative (Rat d) where
  Rat ma < |> Rat mb = Rat $ \ d -> case ma d of
    Fail _ False -> mb d
    x            -> x
  empty = Rat $ \ _ -> Fail "empty" False
 

For those willing to forego the asymptotic guarantees of packrat, we'll offer a monad.

 
instance Monad (Rat d) where
  return a = Rat $ \_ -> Pure a
  Rat m >>= k = Rat $ \d -> case m d of
    Pure a -> runRat (k a) d
    Commit d' a -> case runRat (k a) d' of
      Pure b -> Commit d' b
      Fail s _ -> Fail s True
      commit -> commit
    Fail s c -> Fail s c
  fail s = Rat $ \ _ -> Fail s False
 
instance MonadPlus (Rat d) where
  mplus = (< |>)
  mzero = empty
 

and a Parsec-style "try", which rewinds on failure, so that < |> can try again.

 
try :: Rat d a -> Rat d a
try (Rat m) = Rat $ \d -> case m d of
  Fail s _ -> Fail s False
  x        -> x
 

Since we've consumed < |> with parsec semantics. Let's give a PEG-style backtracking (< />).

 
(< />) :: Rat d a -> Rat d a -> Rat d a
p < /> q = try p < |> q
infixl 3 < />
 

So far nothing we have done involves packrat at all. These are all general purpose recursive descent combinators.

We can define an input stream and a number of combinators to read input.

 
class Stream d where
  anyChar :: Rat d Char
 
whiteSpace :: Stream d => Rat d ()
whiteSpace = () < $ many (satisfy isSpace)
phrase :: Stream d => Rat d a -> Rat d a
phrase m = whiteSpace *> m < * eof
 
notFollowedBy :: Rat d a -> Rat d ()
notFollowedBy (Rat m) = Rat $ \d -> case m d of
  Fail{} -> Pure ()
  _      -> Fail "unexpected" False
 
eof :: Stream d => Rat d ()
eof = notFollowedBy anyChar
 
satisfy :: Stream d => (Char -> Bool) -> Rat d Char
satisfy p = try $ do
  x < - anyChar
  x <$ guard (p x)
 
char :: Stream d => Char -> Rat d Char
char c = satisfy (c ==)
 
lexeme :: Stream d => Rat d a -> Rat d a
lexeme m = m < * whiteSpace
 
symbol :: Stream d => Char -> Rat d Char
symbol c = lexeme (char c)
 
digit :: Stream d => Rat d Int
digit = digitToInt < $> satisfy isDigit
 

And we can of course use a string as our input stream:

 
instance Stream [Char] where
  anyChar = Rat $ \s -> case s of
    (x:xs) -> Commit xs x
    [] -> Fail "EOF" False
 

Now that we've built a poor man's Parsec, let's do something more interesting. Instead of just using String as out input stream, let's include slots for use in memoizing the results from our various parsers at each location. To keep things concrete, we'll memoize the ArithPackrat.hs example that Bryan Ford used in his initial packrat presentation enriched with some whitespace handling.

 
data D = D
  { _add        :: Result D Int
  , _mult       :: Result D Int
  , _primary    :: Result D Int
  , _decimal    :: Result D Int
  , anyCharD    :: Result D Char
  }
 

If you look at the type of each of those functions you'll see that _add :: D -> Result D Int, which is exactly our Rat newtype expects as its argument, we we can bundle them directly:

 
 
add, mult, primary, decimal :: Rat D Int
add     = Rat _add
mult    = Rat _mult
primary = Rat _primary
decimal = Rat _decimal
 

We can similarly juse use the character parse result.

 
instance Stream D where
  anyChar = Rat anyCharD
 

Now we just need to build a D from a String. I'm using view patterns and record wildcards to shrink the amount of repetitive naming.

 
parse :: String -> D
parse s = fix $ \d -> let
  Rat (dv d -> _add) =
        (+) < $> mult < * symbol '+' <*> add
     < /> mult
  Rat (dv d -> _mult) =
        (*) < $> primary < * symbol '*' <*> mult
    < /> primary
  Rat (dv d -> _primary) =
        symbol '(' *> add < * symbol ')'
    </> decimal
  Rat (dv d -> _decimal) =
     foldl' (\b a -> b * 10 + a) 0 < $> lexeme (some digit)
  anyCharD = case s of
    (x:xs) -> Commit (parse xs) x
    []     -> Fail "EOF" False
  in D { .. }
 
dv :: d -> (d -> b) -> b
dv d f = f d
 

Note that we didn't really bother factoring the grammar, since packrat will take care of memoizing the redundant calls!

And with that, we can define an evaluator.

 
eval :: String -> Int
eval s = case runRat (whiteSpace *> add < * eof) (parse s) of
  Pure a -> a
  Commit _ a -> a
  Fail s _ -> error s
 

Note that because the input stream D contains the result directly and parse is the only thing that ever generates a D, and it does so when we start up, it should be obvious that the parse results for each location can't depend on any additional information smuggled in via our monad.

Next time, we'll add a packratted Stream type directly to Parsec, which will necessitate some delicate handling of user state.

The small parser implemented here can be found on my github account, where it hasn't been adulterated with unnecessary spaces by my blog software.

P.S. To explain the quote, had I thought of it earlier, I could have named my parsing combinator library "Kessel Run" as by the time I'm done with it "it will contain at least 12 parsecs" between its different parser implementations.

In particular, I want to talk about a novel encoding of linear functionals, polynomials and linear maps in Haskell, but first we're going to have to build up some common terminology.

Having obtained the blessing of Wolfgang Jeltsch, I replaced the algebra package on hackage with something... bigger, although still very much a work in progress.

(Infinite) Modules over Semirings

Recall that a vector space V over a field F is given by an additive Abelian group on V, and a scalar multiplication operator
(.*) :: F -> V -> V subject to distributivity laws

 
s .* (u + v) = s .* u + s .* v
(s + t) .* v = s .* v + t .* v
 

and associativity laws

 
   (s * t) .* v = s .* (t .* v)
 

and respect of the unit of the field.

 
   1 .* v = v
 

Since multiplication on a field is commutative, we can also add

 
  (*.) :: V -> F -> V
  v *. f = f .* v
 

with analogous rules.

But when F is only a Ring, we call the analogous structure a module, and in a ring, we can't rely on the commutativity of multiplication, so we may have to deal left-modules and right-modules, where only one of those products is available.

We can weaken the structure still further. If we lose the negation in our Ring we and go to a Rig (often called a Semiring), now our module is an additive moniod.

If we get rid of the additive and multiplicative unit on our Rig we get down to what some authors call a Ringoid, but which we'll call a Semiring here, because it makes the connection between semiring and semigroup clearer, and the -oid suffix is dangerously overloaded due to category theory.

First we'll define additive semigroups, because I'm going to need both additive and multiplicative monoids over the same types, and Data.Monoid has simultaneously too much and too little structure.

 
-- (a + b) + c = a + (b + c)
class Additive m where
  (+) :: m -> m -> m
  replicate1p :: Whole n => n -> m -> m -- (ignore this for now)
  -- ...
 

their Abelian cousins

 
-- a + b = b + a
class Additive m => Abelian m
 

and Multiplicative semigroups

 
-- (a * b) * c = a * (b * c)
class Multiplicative m where
  (*) :: m -> m -> m
  pow1p :: Whole n => m -> n -> m
  -- ...
 

Then we can define a semirings

 
-- a*(b + c) = a*b + a*c
-- (a + b)*c = a*c + b*c
class (Additive m, Abelian m, Multiplicative m) => Semiring
 

With that we can define modules over a semiring:

 
-- r .* (x + y) = r .* x + r .* y
-- (r + s) .* x = r .* x + s .* x
-- (r * s) .* x = r .* (s .* x)
class (Semiring r, Additive m) => LeftModule r m
   (.*) :: r -> m -> m
 

and analogously:

 
class (Semiring r, Additive m) => RightModule r m
   (*.) :: m -> r -> m
 

For instance every additive semigroup forms a semiring module over the positive natural numbers (1,2..) using replicate1p.

If we know that our addition forms a monoid, then we can form a module over the naturals as well

 
-- | zero + a = a = a + zero
class
    (LeftModule Natural m,
    RightModule Natural m
    ) => AdditiveMonoid m where
   zero :: m
   replicate :: Whole n => n -> m -> m
 

and if our addition forms a group, then we can form a module over the integers

 
-- | a + negate a = zero = negate a + a
class
    (LeftModule Integer m
    , RightModule Integer m
    ) => AdditiveGroup m where
  negate :: m -> m
  times :: Integral n => n -> m -> m
  -- ...
 

Free Modules over Semirings

A free module on a set E, is a module where the basis vectors are elements of E. Basically it is |E| copies of some (semi)ring.

In Haskell we can represent the free module of a ring directly by defining the action of the (semi)group pointwise.

 
instance Additive m => Additive (e -> m) where
   f + g = \x -> f x + g x
 
instance Abelian m => Abelian (e -> m)
 
instance AdditiveMonoid m => AdditiveMonoid (e -> m) where
   zero = const zero
 
instance AdditiveGroup m => AdditveGroup (e -> m) where
   f - g = \x -> f x - g x
 

We could define the following

 
instance Semiring r => LeftModule r (e -> m) where
   r .* f = \x -> r * f x
 

but then we'd have trouble dealing with the Natural and Integer constraints above, so instead we lift modules

 
instance LeftModule r m => LeftModule r (e -> m) where
   (.*) m f e = m .* f e
 
instance RightModule r m => RightModule r (e -> m) where
   (*.) f m e = f e *. m
 

We could go one step further and define multiplication pointwise, but while the direct product of |e| copies of a ring _does_ define a ring, and this ring is the one provided by the Conal Elliot's vector-space package, it isn't the most general ring we could construct. But we'll need to take a detour first.

Linear Functionals

A Linear functional f on a module M is a linear function from a M to its scalars R.

That is to say that, f : M -> R such that

 
f (a .* x + y) = a * f x + f y
 

Consequently linear functionals also form a module over R. We call this module the dual module M*.

Dan Piponi has blogged about these dual vectors (or covectors) in the context of trace diagrams.

If we limit our discussion to free modules, then M = E -> R, so a linear functional on M looks like (E -> R) -> R
subject to additional linearity constraints on the result arrow.

The main thing we're not allowed to do in our function is apply our function from E -> R to two different E's and then multiply the results together. Our pointwise definitions above satisfy those linearity constraints, but for example:

 
bad f = f 0 * f 0
 

does not.

We could capture this invariant in the type by saying that instead we want

 
newtype LinearM r e =
  LinearM {
    runLinearM :: forall r. LeftModule r m => (e -> m) -> m
  }
 

we'd have to make a new such type every time we subclassed Semiring. I'll leave further exploration of this more exotic type to another time. (Using some technically illegal module instances we can recover more structure that you'd expect.)

Now we can package up the type of covectors/linear functionals:

 
infixr 0 $*
newtype Linear r a = Linear { ($*) :: (a -> r) -> r }
 

The sufficiently observant may have already noticed that this type is the same as the Cont monad (subject to the linearity restriction on the result arrow).

In fact the Functor, Monad, Applicative instances for Cont all carry over, and preserve linearity.

(We lose callCC, but that is at least partially due to the fact that callCC has a less than ideal type signature.)

In addition we get a number of additional instances for Alternative, MonadPlus, by exploiting the knowledge that r is ring-like:

 
instance AdditiveMonoid r => Alternative (Linear r a) where
  Linear f < |> Linear g = Linear (f + g)
  empty = Linear zero
 

Note that the (+) and zero there are the ones defined on functions from our earlier free module construction!

Linear Maps

Since Linear r is a monad, Kleisli (Linear r) forms an Arrow:

 
b -> ((a -> r) ~> r)
 

where the ~> denotes the arrow that is constrained to be linear.

If we swap the order of the arguments so that

 
(a -> r) ~> (b -> r)
 

this arrow has a very nice meaning! (See Numeric.Map.Linear)

 
infixr 0 $#
newtype Map r b a = Map { ($#) :: (a -> r) -> (b -> r) }
 

Map r b a represents the type of linear maps from a -> b. Unfortunately due to contravariance the arguments wind up in the "wrong" order.

 
instance Category (Map r) where
  Map f . Map g = Map (g . f)
  id = Map id
 

So we can see that a linear map from a module A with basis a to a vector space with basis b effectively consists of |b| linear functionals on A.

Map r b a provides a lot of structure. It is a valid instance of an insanely large number of classes.

Vectors and Covectors

In physics, we sometimes call linear functionals covectors or covariant vectors, and if we're feeling particularly loquacious, we'll refer to vectors as contravariant vectors.

This has to do with the fact that when you change basis, you change map the change over covariant vectors covariantly, and map the change over vectors contravariantly. (This distinction is beautifully captured by Einstein's summation notation.)

We also have a notion of covariance and contravariance in computer science!

Functions vary covariantly in their result, and contravariant in their argument. E -> R is contravariant in E. But we chose this representation for our free modules, so the vectors in our free vector space (or module) are contravariant in E.

 
class Contravariant f where
  contramap :: (a -> b) -> f a -> f b
 
-- | Dual function arrows.
newtype Op a b = Op { getOp :: b -> a } 
 
instance Contravariant (Op a) where
  contramap f g = Op (getOp g . f)
 

On the other hand (E -> R) ~> R varies covariantly with the change of E.

as witnessed by the fact that it is a Functor.

 
instance Functor (Linear r) where
  fmap f m = Linear $ \k -> m $* k . f
 

We have lots of classes for manipulating covariant structures, and most of them apply to both (Linear r) and (Map r b).

Other Representations and Design Trade-offs

One common representation of vectors in a free vector space is as some kind of normalized list of scalars and basis vectors. In particular, David Amos's wonderful HaskellForMaths uses

 
newtype Vect r a = Vect { runVect :: [(r,a)] }
 

for free vector spaces, only considering them up to linearity, paying for normalization as it goes.

Given the insight above we can see that Vect isn't a representation of vectors in the free vector space, but instead represents the covectors of that space, quite simply because Vect r a varies covariantly with change of basis!

Now the price of using the Monad on Vect r is that the monad denormalizes the representation. In particular, you can have multiple copies of the same basis vector., so any function that uses Vect r a has to merge them together.

On the other hand with the directly encoded linear functionals we've described here, we've placed no obligations on the consumer of a linear functional. They can feed the directly encoded linear functional any vector they want!

In fact, it'll even be quite a bit more efficient to compute,

To see this, just consider:

 
instance MultiplicativeMonoid r => Monad (Vect r) where
   return a = Vect [(1,a)]
   Vect as >>= f = Vect
       [ (p*q, b) | (p,a) < - as, (q,b) <- runVect (f b) ]
 

Every >>= must pay for multiplication. Every return will multiply the element by one. On the other hand, the price of return and bind in Linear r is function application.

 
instance Monad (Linear r) where
  return a = Linear $ \k -> k a
  m >>= f = Linear $ \k -> m $* \a -> f a $* k
 

A Digression on Free Linear Functionals

To wax categorical for a moment, we can construct a forgetful functor U : Vect_F -> Set that takes a vector space over F to just its set of covectors.

 
F E = (E -> F, F,\f g x -> f x + g x ,\r f x -> r * f x)
 

using the pointwise constructions we built earlier.

Then in a classical setting, you can show that F is left adjoint to U.

In particular the witnesses of this adjunction provide the linear map from (E -> F) to V and the function E -> (V ~> F) giving a linear functional on V for each element of E.

In a classical setting you can go a lot farther, and show that all vector spaces (but not all modules) are free.

But in a constructive setting, such as Haskell, we need a fair bit to go back and forth, in particular we wind up need E to be finitely enumerable to go one way, and for it to have decidable equality to go in the other. The latter is fairly easy to see, because even going from E -> (E -> F) requires that we can define and partially apply something like Kronecker's delta:

 
delta :: (Rig r, Eq a) => e -> e -> r
delta i j | i == j = one
             | otherwise = zero
 

The Price of Power

The price we pay is that, given a Rig, we can go from Vect r a to Linear r a but going back requires a to be be finitely enumerable (or for our functional to satisfy other exotic side-conditions).

 
vectMap :: Rig r => Vect r a -> Linear r a
vectMap (Vect as) = Map $ \k -> sum [ r * k a | (r, a) < - as ]
 

You can still probe Linear r a for individual coefficients, or pass it a vector for polynomial evaluation very easily, but for instance determining a degree of a polynomial efficiently requires attaching more structure to your semiring, because the only value you can get out of Linear r a is an r.

Optimizing Linear Functionals

In both the Vect r and Linear r cases, excessive use of (>>=) without somehow normalizing or tabulating your data will cause a lot of repeated work.

This is perhaps easiest to see from the fact that Vect r never used the addition of r, so it distributed everything into a kind of disjunctive normal form. Linear r does the same thing.

If you look at the Kleisli arrows of Vect r or Linear r as linear mappings, then you can see that Kleisli composition is going to explode the number of terms.

So how can we collapse back down?

In the Kleisli (Vect r) case we usually build up a map as we walk through the list then spit the list back out in order having added up like terms.

In the Map r case, we can do better. My representable-tries package provides a readily instantiable HasTrie class, and the method:

 
memo :: HasTrie a => (a -> r) -> a -> r
 

which is responsible for providing a memoized version of the function from a -> r in a purely functional way. This is obviously a linear map!

 
memoMap :: HasTrie a => Map r a a
memoMap = Map memo
 

We can also flip memo around and memoize linear functionals.

 
memoLinear :: HasTrie a => a -> Linear r a
memoLinear = Linear . flip memo
 

Next time, (co)associative (co)algebras and the myriad means of multiplying (co)vectors!

The question I hope to answer this time, is whether or not we turn any Haskell Comonad into a comonad transformer.

Comonads from Comonads

In Monads from Comonads, we built the comonad-to-monad transformer

 
newtype Co w m a = Co (forall r. w (a -> r) -> r)
 

by sandwiching a Comonad w in the middle of a trivial Codensity monad, then proceeded to show that at least in the case where our comonad was given rise to by an adjunction f -| g : Hask -> Hask, we could reason about this as if we had

 
Co w ~ Co (f . g) ~ g . f
 

Now, Codensity monads are a right Kan extension.

So, what happens if we try to do the same thing to a Left Kan extension?

Using

 
{-# LANGUAGE GADTs, FlexibleInstances #-}
 
import Control.Comonad
import Control.Comonad.Trans.Class
 

we can define

 
data L w a where
  L :: w (r -> a) -> r -> L w a
 

and a number of instances pop out for free, cribbed largely from the definition for Density.

 
instance Functor w => Functor (L w) where
  fmap f (L w r) = L (fmap (f .) w) r
 
instance ComonadTrans L where
  lower (L w r) = fmap ($r) w
 
instance Extend w => Extend (L w) where
  duplicate (L w s) = L (extend L w) s
 
instance Comonad w => Comonad (L w) where
  extract (L w r) = extract w r
 

Reasoning as before about w as if it were composed of an adjunction f -| g : Hask -> Hask to build some intuition, we can see:

 
L w a ~ exists r. (w (r -> a), r)
      ~ exists r. (f (g (r -> a)), r)
      ~ exists r. (f (), g (r -> a), r)
      ~ exists r. (f (), f () -> r -> a, r)
      ~ exists r. (f () -> r -> a, f r)
      ~ exists r. (f r -> a, f r)
      ~ Density f a
      ~ Lan f f a
      ~ (f . Lan f Id) a
      ~ (f . g) a
      ~ w a
 

The latter few steps require identities established in my second post on Kan extensions.

With that we obtain the "remarkable" insight that L ~ IdentityT, which I suppose is much more obvious when just looking at the type

 
data L w a where
  L :: w (r -> a) -> r -> L w a
 

and seeing the existentially quantified r as a piece of the environment, being used to build an a, since there is nothing else we can do with it, except pass it in to each function wrapped by w! So at first blush, we've gained nothing.

The key observation is that in one case we would up with something isomorphic to the codensity monad of our right adjoint, while in the other case we would up with the density comonad of our left adjoint. The former is isomorphic to the monad given by our adjunction, while the latter is isomorphic to the comonad, which is, unfortunately, right where we started!

In The Future All Comonads are Comonad Transformers!

Of course, we don't have to just modify a trivial left Kan extension. Let's tweak the Density comonad of another comonad!

 
data D f w a where
  D :: w (f r -> a) -> f r -> D f w a
 

Since both arguments will be comonads, and I want this to be a comonad transformer, I'm going to swap the roles of the arguments relative to the definition of CoT w m. The reason is that D f w is a Comonad, regardless of the properties of f, so long as w is a Comonad This is similar to how Density f is a Comonad regardless of what f is, as long as it has kind * -> *.

The implementation of D is identical to L above, just as CoT and Co share implementations and ContT and Cont do.

 
instance Functor w => Functor (D f w) where
  fmap f (D w r) = D (fmap (f .) w) r
 
instance Extend w => Extend (D f w) where
  duplicate (D w s) = D (extend D w) s
 
instance Comonad w => Comonad (D f w) where
  extract (D w r) = extract w r
 
instance ComonadTrans (D f) where
  lower (D w r) = fmap ($r) w
 

But in addition to being able to lower :: D f w a -> w a, we can also lower to the other comonad!

 
fstD :: (Extend f, Comonad w) => D f w a -> f a
fstD (D w r) = extend (extract w) r
 
sndD :: Comonad w => D f w a -> w a
sndD = lower
 

This means that if either comonad provides us with a piece of functionality we can exploit it.

Selling Products

In general Monad products always exist:

newtype Product m n a = Pair { runFst :: m a, runSnd :: n a }

instance (Monad m, Monad n) => Monad (Product m n) where
   return a = Pair (return a) (return a)
   Pair ma na >>= f = Pair (ma >>= runFst . f) (na >>= runSnd . f)

and Comonad coproducts always exist:

 
newtype Coproduct f g a = Coproduct { getCoproduct :: Either (f a) (g a) }
 
left :: f a -> Coproduct f g a
left = Coproduct . Left
 
right :: g a -> Coproduct f g a
right = Coproduct . Right
 
coproduct :: (f a -> b) -> (g a -> b) -> Coproduct f g a -> b
coproduct f g = either f g . getCoproduct
 
instance (Extend f, Extend g) => Extend (Coproduct f g) where
  extend f = Coproduct . coproduct
    (Left . extend (f . Coproduct . Left))
    (Right . extend (f . Coproduct . Right))
 
instance (Comonad f, Comonad g) => Comonad (Coproduct f g) where
  extract = coproduct extract extract
 

but Christoph Lüth and Neil Ghani showed that monad coproducts don't always exist!

On the other hand what we built up above looks a lot like a comonad product!

Too see that, first we'll note some of the product-like things we can do:

fstD and sndD act a lot like fst and snd, projecting our parts of our product and it turns out we can "braid" our almost-products, interchanging the left and right hand side.

 
braid :: (Extend f, Comonad w) => D f w a -> D w f a
braid (D w r) = D (extend (flip extract) r) w
 

(I use scary air-quotes around braid, because it doesn't let us braid them in a categorical sense, as we'll see.)

After braiding, one of our projections swaps places as we'd expect:

 
sndD (braid (D w r)) = -- by braid def
sndD (D (extend (flip extract) r) w) = -- by sndD (and lower) def
fmap ($w) (extend (flip extract) r) = -- extend fmap fusion
extend (($w) . flip extract) r = -- @unpl
extend (\t -> flip extract t w) r = -- flip . flip = id
extend (extract w) r = -- by fstD def
fstD (D w r)
 

But we stall when we try to show fstD . braid = sndD.

Why is that?

A Product of an Imperfect Union

Last time, when we inspected CoT w m a we demonstrated that on one hand given a suitable adjunction f -| g, such that w = f . g, Co w ~ Co (f . g) ~ (g . f), but on the other CoT w m a was bigger than g . m . f, and that if n -| m, then CoT w m a ~ g . m . n . f.

Of course, these two results agree, if you view Co w as CoT w Identity, where Identity -| Identity, since Identity ~ Identity . Identity

Therefore it should come as no surprise that given w = f . g, for a suitable adjunction f -| g, then D w j a is bigger than f . j . g. In fact if, j -| k, then D w j ~ f . j . k . g.

So what is happening is that we have only managed to "break one of our comonads in half", and D w j a lets you do 'too much stuff' with the j portion of the comonad. This keeps us from being symmetric.

Moreover it turns out to be a bit trickier to build one than to just hand in a w (f a) or w a and an f a to build our product-like construction.

Even so, exploiting Density was enough to transform any comonad into a comonad-transformer and to enable us to access the properties of either the comonad we are transforming with, or the comonad that we are transforming.