> {-# LANGUAGE FlexibleInstances #-}

> import Data.Monoid

> import Control.Monad.Writer

> import Control.Monad.State

Let's define a simple tree type:

> data Tree a = Leaf a | Tree [Tree a] deriving (Eq,Show)

Sometimes we want to apply a function to every element of the tree. That's provided by the

`fmap`member of the

`Functor`type class.

> instance Functor Tree where

> fmap f (Leaf x) = Leaf (f x)

> fmap f (Tree ts) = Tree $ map (fmap f) ts

But just as we can't use map to apply monadic functions to a list (we'd write

`mapM print [1,2,3]`), we can't use fmap to apply them to our tree. What we need is a monadic version of

`fmap`. Here's a suitable type class:

> class FunctorM c where

> fmapM :: Monad m => (a -> m b) -> c a -> m (c b)

(I could have used Data.Traversable but that entails Data.Foldable and that's too much work.)

And now we can implement a monadic version of

`Tree`'s

`Functor`instance:

> instance FunctorM Tree where

> fmapM f (Leaf x) = do

> y <- f x

> return (Leaf y)

> fmapM f (Tree ts) = do

> ts' <- mapM (fmapM f) ts

> return (Tree ts')

We can use

`fmapM`to extract the list of elements of a container type:

> serialise a = runWriter $ fmapM putElement a

> where putElement x = tell [x]

Not only does

`serialise`suck out the elements of a container, it also spits out an empty husk in which all of the elements have been replaced by

`()`. We can think of the latter as the 'shape' of the original structure with the original elements removed. We can formalise this as

> type Shape t = t ()

So we have:

> serialise :: FunctorM t => t a -> (Shape t,[a])

Keeping the shape around allows is to invert

`serialise`to give:

> deserialise :: FunctorM t => Shape t -> [a] -> (t a, [a])

> deserialise t = runState (fmapM getElement t) where

> getElement () = do

> x:xs <- get

> put xs

> return x

(That's a bit like using the supply monad. This function also returns the leftovers.)

We can also write (apologies for the slightly cryptic use of the writer monad):

> size :: FunctorM t => t a -> Int

> size a = getSum $ execWriter $ fmapM incCounter a

> where incCounter _ = tell (Sum 1)

Let's try an example. Here's an empty tree:

> tree1 = Tree [Tree [Leaf (),Leaf ()],Leaf ()]

We can pack a bunch of integers into it:

> ex1 = fst $ deserialise tree1 [1,2,3]

And get them back out again:

> ex2 = serialise ex1

`serialise`separates the shape from the data, something you can read lots more about at Barry Jay's web site.

Remember that trees are also monads:

> instance Monad Tree where

> return x = Leaf x

> t >>= f = join (fmap f t) where

> join (Leaf t) = t

> join (Tree ts) = Tree (fmap join ts)

The

`join`operation for a tree grafts the elements of a tree of trees back into the tree.

Right, that's enough about trees and shapes for now.

Operads are a bit like the plumbing involved in installing a sprinkler system. Suppose you have a piece, A that splits a single pipe into two:

______

/

/ ____

____/ /

A /

____ \

\ \____

\

\______

And you have two more pipes B and C that look like this:

______

/

/ ____

/ /

____/ \____

B or C

____ ____

\ /

\ \____

\

\______

then you can 'compose' them to make a larger system like this:

______

/

/ ____

/ /

_________/ \____

/ B

/ _______ ____

/ / \ /

/ / \ \____

/ / \

____/ / \______

A /

____ \ ______

\ \ /

\ \ / ____

\ \ / /

\ \_______/ \____

\ C

\_________ ____

\ /

\ \____

\

\______

(Vim rectangular mode FTW!)

The important thing to note here is that as A had two outputs (or inputs, depending on your point of view) you can attach two more pieces, like B and C, directly to it.

Call the number of outlets the 'degree' of the system. If A has degree n then we can attach n more systems, A

_{1}...A

_{n}to it and the resulting system will have degree degree(A

_{1})+...+degree(A

_{n}). We can write the result as A(A

_{1},...,A

_{n}).

We also have the 'identity' pipe that looks like this:

_____________

identity

_____________

Formally, an operad is a collection of objects, each of which has a 'degree' that's an integer n, n≥0, depending on your application), an identity element of degree 1, and a composition law:

> class Operad a where

> degree :: a -> Int

> identity :: a

> o :: a -> [a] -> a

`o`is the composition operation. If f has degree n then we can apply it to a list of n more objects. So we only expect to evaluate

`f `o` fs`successfully if

`degree f == length fs`.

There are many identities we'd expect to hold. For example

`f `o` [identities,...,identity] == f`, because adding plain sections of pipe has no effect. We also expect some associativity conditions coming from the fact that it doesn't matter what order we build a pipe assembly in, it'll still function the same way.

We can follow this pipe metaphor quite closely to define what I think of as the prototype Operad. A

`Fn a`is a function that takes n inputs of type a and returns one of type a. As we can't easily introspect and find out how many arguments such a function expects, we also store the degree of the function with the function:

> data Fn a = F { deg::Int, fn::[a] -> a }

> instance Show a => Show (Fn a) where

> show (F n _) = "<degree " ++ show n ++ " function>"

`unconcat`is a kind of inverse to

`concat`. You give a list of integers and it chops up a list into pieces with lengths corresponding to your integers. We use this to unpack the arguments to

`f `o` gs`into pieces suitable for the elements of

`gs`to consume.

> unconcat [] [] = []

> unconcat (n:ns) xs = take n xs : unconcat ns (drop n xs)

> instance Operad (Fn a) where

> degree = deg

> f `o` gs = let n = sum (map degree gs)

> in F n (fn f . zipWith fn gs . unconcat (map degree gs))

> identity = F 1 head

Now compute an example, f(f

_{1},f

_{2},f

_{3}) applied to [1,2,3]. It should give 1+1+2*3=8.

> ex3 = fn (f `o` [f1,f2]) [1,2,3] where

> f = F 2 (\[x,y] -> x+y)

> f1 = F 1 (\[x] -> x+1)

> f2 = F 2 (\[x,y] -> x*y)

(That's a lot like lambda calculus without names. Operads are a bit like n-ary combinators.)

Now I'm going to introduce a different looking operad. Think of

`V`as representing schemes for dicing the real line. Here are some examples:

|0.25|0.25| 0.5 |

|0.1|0.1| 0.8 |

If A divides up the real line into n pieces then you could divide up each of the n pieces using their own schemes. This means that dicing schemes compose. So if we define A, B and C as:

A = |0.5|0.5|

B = |0.75|0.25|

C = |0.1|0.1|0.8|

Then A(B,C) is:

|0.375|0.125|0.05|0.05|0.4|

We could implement

`V`as a list of real numbers, but it's more fun to generalise to any monoid and not worry about divisions summing to 1:

> data V m = V { unV :: [m] } deriving (Eq,Show)

This becomes an operad by allowing the monoid value in a 'parent' scheme multiply the values in a 'child'.

> instance Monoid m => Operad (V m) where

> degree (V ps) = length ps

> (V as) `o` bs = V $ op as (map unV bs) where

> op [] [] = []

> op (a:as) (b:bs) = map (mappend a) b ++ op as bs

> identity = V [mempty]

For example, if d

_{1}cuts the real line in half, and d

_{2}cuts it into thirds, then d

_{1}(d

_{1},d

_{2}) will cut it into five pieces of lengths 1/4,1/4,1/6,1/6,1/6:

> ex4 = d1 `o` [d1,d2] where

> d1 = V [Product (1/2),Product (1/2)]

> d2 = V [Product (1/3),Product (1/3),Product (1/3)]

If the elements in a

`V`are non-negative and sum to 1 we can think of them as probability distributions. The composition A(A

_{1},...,A

_{n}) is the distribution of all possible outcomes you can get by selecting a value i in the range {1..n} using distribution A and then selecting a second

value conditionally from distribution A

_{i}. We connect with the recent n-category post on entropy.

In fact we can compute the entropy of a distrbution as follows:

> h (V ps) = - (sum $ map (\(Product x) -> xlogx x) ps) where

> xlogx 0 = 0

> xlogx x = x*log x/log 2

We can now look at the 'aside' in that post. From an element of

`V`we can produce a function that computes a corresponding linear combination (at least for

`Num`types):

> linear (V ps) xs = sum $ zipWith (*) (map (\(Product x) -> x) ps) xs

We can now compute the entropy of a distribution in two different ways:

> (ex5,ex6) = (h (d1 `o` [d1,d2]),h d1 + linear d1 (map h [d1,d2])) where

> d1 = V [Product 0.5,Product 0.5]

> d2 = V [Product 0.25,Product 0.75]

Now according to this paper on operads we can build a monad from an operad. Here's the construction:

> data MonadWrapper op a = M { shape::op, value::[a] } deriving (Eq,Show)

(The field names aren't from the paper but they do give away what's actually going on...)

The idea is that an element of this construction consists of an element of the operad of degree n, and an n element list. It's a functor in an obvious way:

> instance Functor (MonadWrapper o) where

> fmap f (M o xs) = M o (map f xs)

It's also a

`FunctorM`:

> instance FunctorM (MonadWrapper o) where

> fmapM f (M s c) = do

> c' <- mapM f c

> return $ M s c'

We can make the construction a monad as follows:

> instance Operad o => Monad (MonadWrapper o) where

> return x = M identity [x]

> p >>= f = join (fmap f p) where

> join (M p xs) = M (p `o` map shape xs) (concatMap value xs)

Now for something to be a monad there are various laws that needs to be satisfied. These follow from the rules (which I haven't explicitly stated) for an operad. When I first looked at that paper I was confused - it seemed that the operad part and the list part didn't interact with each other. And then I suddenly realised what was happening. But hang on for a moment...

Tree shapes make nice operads. The composition rule just grafts child trees into the leaves of the parent:

> instance Operad (Tree ()) where

> degree t = length (snd (serialise t))

> identity = Leaf ()

> t `o` ts = let (r,[]) = deserialise t ts in r >>= id

We can write that more generically so it works with more than trees:

> data OperadWrapper m = O { unO::Shape m }

> instance (FunctorM m,Monad m) => Operad (OperadWrapper m) where

> degree (O t) = size t

> identity = O (return ())

> (O t) `o` ts = let (r,[]) = deserialise t (map unO ts) in O (r >>= id)

So let's use the construction above to make a monad. But what actually is this monad? Each element is a pair with (1) a tree shape of degree n and (2) an n-element list. In other words, it's just a serialised tree. We can define these isomorphisms to make that clearer:

> iso1 :: FunctorM t => t x -> MonadWrapper (t ()) x

> iso1 t = uncurry M (serialise t)

> iso2 :: FunctorM t => MonadWrapper (t ()) x -> t x

> iso2 (M shape contents) = let (tree,[]) = deserialise shape contents in tree

So, for example:

> ex7 = iso2 (iso1 tree) where

> tree = Tree [Tree [Leaf "Birch",Leaf "Oak"],Leaf "Cypress",Leaf "Binary"]

That construction won't work for all monads, just those monads that come from operads. I'll leave you to characterise those.

And now we have it: a way to think about operads from a computational perspective. They're the shapes of certain monadic serialisable containers. Operadic composition is the just the same grafting operation used in the

`join`operation, using values

`()`as the graft points.

I have a few moments spare so let's actually do something with an operad. First we need the notion of a free operad. This is basically just a set of 'pipe' parts that we can stick together with no equations holding apart from those inherent in the definition of an operad. This is different from the

`V`operad where many different ways of apply the

`o`operator can result in the same result. We can use any set of parts, as long as we can associate an integer with each part:

> class Graded a where

> grade :: a -> Int

The free operad structure is just a tree:

> data FreeOperad a = I | B a [FreeOperad a] deriving Show

`I`will be the identity, but it will also serve as a 'terminator' like the way there's always a

`[]`at the end of a list.

An easy way to make a single part an element of an operad:

> b n = let d = grade n in B n (replicate d I)

Here's the instance:

> instance Graded a => Operad (FreeOperad a) where

> degree I = 1

> degree (B _ xs) = sum (map degree xs)

> identity = I

> I `o` [x] = x

> B a bs `o` xs = let arities = map degree bs

> in B a $ zipWith o bs (unconcat arities xs)

Now I'm going to use this to make an operad and then a monad:

> instance Graded [a] where

> grade = length

> type DecisionTree = MonadWrapper (FreeOperad [Float])

What we get is a lot like the probability monad except it doesn't give the final probabilities. Instead, it gives the actual tree of possibilities. (I must also point out this hpaste by wli.)

> test = do

> a <- M (b [Product 0.5,Product 0.5]) [1,2]

> b <- M (b [Product (1/3.0),Product (1/3.0),Product (1/3.0)]) [1,2,3]

> return $ a+b

Now we can 'flattten' this tree so that the leaves have the final probabilities:

> flatten :: Monoid m => FreeOperad [m] -> V m

> flatten I = V [mempty]

> flatten (B ms fs) = V $ concat $ zipWith (map . mappend) ms (map (unV . flatten) fs)

This is a morphism of operads. (You can probably guess the definition of such a thing.) It induces a morphism of monads:

> liftOp :: (Operad a,Operad b) => (a -> b) -> MonadWrapper a x -> MonadWrapper b x

> liftOp f (M shape values) = M (f shape) values

`liftOp flatten test`will give you the final probabilities.

There may just be a possible application of this stuff. The point of separating shape from data is performance. You can store all of your data in flat arrays and do most of your work there. It means you can write fast tight loops and only rebuild the original datatype if needed at the end. If you're lucky you can precompute the shape of the result, allowing you to preallocate a suitable chunk of memory for your final answer to go into. What the operad does is allow you to extend this idea to monadic computations, for suitable monads. If the 'shape' of the computation is independent of the details of the computation, you can use an operad to compute that shape, and then compute the contents of the corresponding array separately. If you look at the instance for

`MonadWrapper`you'll see that the part of the computation that deals with the data is simply a

`concatMap`.

BTW In some papers the definition restricts the degree to ≥1. But that's less convenient for computer science applications. If it really bothers you then you can limit yourself to thinking about containers that contain at least one element.