I permit myself to observe that your powerset problem (and the restricted length problem, i.e. the combinations) is usually solved in Prolog, through backtracking, using reasoning/style which adopts this "individualistic" philosophy.
powerset(<source>,<possible result>) --- is the pattern. And the solution is
powerset([],[]). Since nothing else can be done. Otherwise you pick the item or not.
powerset([X|Rest],L) :- powerset(Rest,L). powerset([X|Rest],[X|L) :- powerset(Rest,L).
The xxx ++ map (x :) xxx solution in Haskell is a particular formulation (and optimization) of the straightforward transformation from a version using the non-deterministic Monad. This one is really almost a carbon copy of the Prolog solution, with appropriate "lifting" of operations from individuals to lazy lists.
I was thinking some more about this comment of yours, and my own experience with the ease of using lists to implement prolog-style generators, and think I come to some better understanding. If I'm right, I assume the following is common knowledge to experienced Haskell programmers. So, in the spirit of testing my understanding...
The common thread here is a non-deterministic calculation in which there are several possible solutions for some problem. The goal is to find (a) if there are any solutions, and (b) one, more or all of the solutions.
Prolog does this, absent ! (cut), by backtracking through the possible solutions.
My first epiphany is that the Haskell idea of using a lazily evaluated list for result of a non-deterministic computation is pretty much the same thing (which is pretty much what you said? Is this what you mean by "the non-deterministic monad"?). The mechanisms for accessing a list mean that the solutions must be accessed in the order they are generated, just like Prolog backtracking.
So there seems to be a very close relationship between the lazy list and non-deterministic computations, but what about other data structures? I speculate that other structures, lazily evaluated, may also be used to represent the results of non-deterministic computations, yet allow the results to be accessed in a different order. And these, too, may be (should be?) monads. If so, the Haskell approach might be viewed as a generalization of Prolog's essentially sequential backtracking.
In a private message concerning the powerset thread on this list, a correspondent offered a program to evaluate the subsets in size order, which I found particularly elegant:
>ranked_powerset :: [a] -> [[[a]]]
>ranked_powerset = takeWhile (not . null) . foldr next_powerset ([[]] : repeat [])
>
>next_powerset :: a -> [[[a]]] -> [[[a]]]
>next_powerset x r = zipWith (++) ([] : map (map (x:)) r) r
>
>powerset :: [a] -> [[a]]
>powerset = tail . concat . ranked_powerset
They also pointed out that "ranked_powerset is handy since you can use it to define combinatorial choice etc.":
> choose :: Int -> [a] -> [[a]] > choose k = (!! k) . ranked_powerset
So here is an example of a different structure (a list of lists) also used to represent a non-deterministic computation, and furthermore providing means to access the results in some order other than a single linear sequences (e.g. could be used to enumerate all the powersets containing the nth member of the base set, *or* all the powersets of a given size, without evaluating all of the other powersets).
To test this idea, I think it should be possible to define a monad based on a simple tree structure, which also can be used to represent the results of a non-deterministic computation. An example of this is below, at the end of this message, which seems to exhibit the expected properties.
So if the idea of representing a non-deterministic computation can be generalized from a list to a tree, why not to other data structures? My tree monad is defined wholly in terms of reduce and fmap. Without going through the exercise, I think the reduce function might be definable in terms of fmap for any data type of the form "Type (Maybe a)", hence applicable to a range of functors? In particular, I'm wondering if it can be applied to any gmap-able structure over a Maybe type.
I'm not sure if this is of any practical use; rather it's part of my attempts to understand the relationship between functors and monads and other things functional.
#g --
[[ -- spike-treemonad.hs
data Tree a = L (Maybe a) | T { l,r :: Tree a }
deriving Eq instance (Show a) => Show (Tree a) where
show t = (showTree "" t) ++ "\n" showTree :: (Show a) => String -> Tree a -> String
showTree _ (L Nothing ) = "()"
showTree _ (L (Just a)) = show a
showTree i (T l r) = "( " ++ (showTree i' l) ++ "\n" ++
i' ++ (showTree i' r) ++ " )"
where i' = ' ':' ':i instance Functor Tree where
fmap f (L Nothing) = L Nothing
fmap f (L (Just a)) = L (Just (f a))
fmap f (T l r) = T (fmap f l) (fmap f r)reduce :: Tree (Tree a) -> Tree a reduce (L Nothing) = L Nothing reduce (L (Just (L Nothing ) )) = L Nothing reduce (L (Just (L (Just a)) )) = L (Just a) reduce (L (Just (T l r ) )) = T l r reduce (T l r) = T (reduce l) (reduce r)
instance Monad Tree where
-- L Nothing >>= k = L Nothing
t >>= k = reduce $ fmap k t
return x = L (Just x)
fail s = L Nothing-- tests
t1 :: Tree String
t1 = T (L $ Just "1")
(T (T (T (L $ Just "211")
(L $ Just "311"))
(L Nothing))
(L $ Just "22"))k1 :: a -> Tree a k1 n = T (L $ Just n) (L $ Just n)
r1 = t1 >>= k1
k2 :: String -> Tree String
k2 s@('1':_) = L $ Just s
k2 s@('2':_) = T (L $ Just s) (L $ Just s)
k2 _ = L Nothingr2 = t1 >>= k2
t3 = L Nothing :: Tree String r3 = t3 >>= k1
r4 = t1 >>= k1 >>= k2 r5 = (return "11") :: Tree String r6 = r5 >>= k1
-- Check out monad laws -- return a >>= k = k a m1a = (return t1) >>= k1 m1b = k1 t1 m1c = m1a == m1b
-- m >>= return = m m2a = t1 >>= return m2b = t1 m2c = m2a == m2b
-- m >>= (\x -> k x >>= h) = (m >>= k) >>= h m3a = t1 >>= (\x -> k1 x >>= k2) m3b = (t1 >>= k1) >>= k2 m3c = m3a == m3b ]]
------------------- Graham Klyne <[EMAIL PROTECTED]> PGP: 0FAA 69FF C083 000B A2E9 A131 01B9 1C7A DBCA CB5E
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