I'm trying to understand histomorphisms from this blog on recursion schemes. I'm facing a problem when I'm running the example to solve the change making problem as mentioned in the blog.
Change making problem takes the denominations for a currency and tries to find the minimum number of coins required to create a given sum of money. The code below is taken from the blog and should compute the answer.
{-# LANGUAGE DeriveFunctor #-}
module Main where
import Control.Arrow ( (>>>) )
import Data.List ( partition )
import Prelude hiding (lookup)
newtype Term f = In {out :: f (Term f)}
data Attr f a = Attr
{ attribute :: a
, hole :: f (Attr f a)
}
type CVAlgebra f a = f (Attr f a) -> a
histo :: Functor f => CVAlgebra f a -> Term f -> a
histo h = out >>> fmap worker >>> h
where
worker t = Attr (histo h t) (fmap worker (out t))
type Cent = Int
coins :: [Cent]
coins = [50, 25, 10, 5, 1]
data Nat a
= Zero
| Next a
deriving (Functor)
-- Convert from a natural number to its foldable equivalent, and vice versa.
expand :: Int -> Term Nat
expand 0 = In Zero
expand n = In (Next (expand (n - 1)))
compress :: Nat (Attr Nat a) -> Int
compress Zero = 0
compress (Next (Attr _ x)) = 1 compress x
change :: Cent -> Int
change amt = histo go (expand amt)
where
go :: Nat (Attr Nat Int) -> Int
go Zero = 1
go curr@(Next attr) =
let given = compress curr
validCoins = filter (<= given) coins
remaining = map (given -) validCoins
(zeroes, toProcess) = partition (== 0) remaining
results = sum (map (lookup attr) toProcess)
in length zeroes results
lookup :: Attr Nat a -> Int -> a
lookup cache 0 = attribute cache
lookup cache n = lookup inner (n - 1) where (Next inner) = hole cache
Now if you evaluate change 10 it will give you 3.
Which is... incorrect because you can make 10 using 1 coin of value 10.
So I considered maybe it's solving the coin change problem, which finds the maximum number of ways in which you can make the given sum of money. For e.g. you can make 10 in 4 ways with { 1, 1, ... 10 times }, { 1, 1, 1, 1, 5}, { 5, 5 }, { 10 }.
So what is wrong with this piece of code? Where is it going wrong in solving the problem?
TLDR
The above piece of code from this blog on recursion schemes is not finding minimum or maximum ways to change a sum of money. Why is it not working?
CodePudding user response：
The initial confusion with the blog post was because it was pointing to a different problem in the wikipedia link.
Retaking a look at change, it's trying to find the number of "ordered" ways of making change for a given value. This means that the ordering of coins matters. The correct value of change 10 should be 9.
Coming back to the problem, the main issue is with the implementation of the lookup method. The key point to note is that lookup is backwards i.e to calculate the contribution of a denomination to the sum it should be passed as argument to the lookup and not it's difference with the given value.
-- to find contribution of 5 to the number of ways we can
-- change 15. We should pass the cache of 15 and 5 as the
-- parameters. So the cache will be unrolled 5 times to
-- to get the value from cache of 10
lookup :: Attr Nat a -- ^ cache
-> Int -- ^ how much to roll back
-> a
lookup cache 1 = attribute cache
lookup cache n = lookup inner (n - 1) where (Next inner) = hole cache
The complete solution is described in this issue by @howsiwei.
Edit: Base on discussion in the comments this can be solved using histomorphisms but with a few challenges
It can be solved using histomorphisms but the cache and functor types will need to be more complex to hold more state. Namely -
- The cache will need to keep a list of permitted denominations for a particular amount this will allow us eliminate overlap
- The harder challenge is to come up with a functor that can order all the information.
Natwill not be sufficient because it cannot distinguish between different values of a complex cache type.
CodePudding user response：
I see two problems with this program. One of them I know how to fix, but the other apparently requires more knowledge of recursion schemes than I have.
The one I can fix is that it's looking up the wrong values in its cache. When given = 10, of course validCoins = [10,5,1], and so we find (zeroes, toProcess) = ([0], [5,9]). So far so good: we can give a dime directly, or give a nickel and then make change for the remaining five cents, or we can give a penny and change the remaining nine cents. But then when we write lookup 9 attr, we're saying "look 9 steps in history to when curr = 1", where what we meant was "look 1 step into history to when curr = 9". As a result we drastically undercount in pretty much all cases: even change 100 is only 16, while a Google search claims the right result is 292 (I haven't verified this today by implementing it myself).
There are a few equivalent ways to fix this; the smallest diff would be to replace
results = sum (map (lookup attr)) toProcess)
with
results = sum (map (lookup attr . (given -)) toProcess)
The second problem is: the values in the cache are wrong. As I mentioned in a comment on the question, this counts different orderings of the same denominations as separate answers to the question. After I fix the first problem, the lowest input where this second problem manifests is 7, with the incorrect result change 7 = 3. If you try change 100 I don't know how long it takes to compute: much longer than it should, probably a very long time. But even a modest value like change 30 yields a number that's much larger than it should be.
I don't see a way to fix this without a substantial algorithm rework. Traditional dynamic-programming solutions to this problem involve producing the solutions in a specific order so you can avoid double-counting. i.e., they first decide how many dimes to use (here, 0 or 1), then compute how to make change for the remaining amounts without using any dimes. I don't know how to work that idea in here - your cache key would need to be larger, including both the target amount and also the allowed set of coins.
CodePudding user response：
I put some more thought into encoding this problem with recursion schemes. Maybe there's a good way to solve the unordered problem (i.e., considering 5c 1c to be different from 1c 5c) using a histomorphism to cache the undirected recursive calls, but I don't know what it is. Instead, I looked for a way to use recursion schemes to implement the dynamic-programming algorithm, where the search tree is probed in a specific order so that you're sure you never visit any node more than once.
The tool that I used is the hylomorphism, which comes up a bit later in the article series you're reading. It composes an unfold (anamorphism) with a fold (catamorphism). A hylomorphism uses ana to build up an intermediate structure, and then cata to tear it down into a final result. In this case, the intermediate structure I used describes a subproblem. It has two constructors: either the subproblem is solved already, or there is some amount of money left to make change for, and a pool of coin denominations to use:
data ChangePuzzle a = Solved Cent
| Pending {spend, forget :: a}
deriving Functor
type Cent = Int
type ChangePuzzleArgs = ([Cent], Cent)
We need a coalgebra that turns a single problem into subproblems:
divide :: Coalgebra ChangePuzzle ChangePuzzleArgs
divide (_, 0) = Solved 1
divide ([], _) = Solved 0
divide (coins@(x:xs), n) | n < 0 = Solved 0
| otherwise = Pending (coins, n - x) (xs, n)
I hope the first three cases are obvious. The last case is the only one with multiple subproblems. We can either use one coin of the first listed denomination, and continue to make change for that smaller amount, or we can leave the amount the same but reduce the list of coin denominations we're willing to use.
The algebra for combining subproblem results is much simpler: we simply add them up.
conquer :: Algebra ChangePuzzle Cent
conquer (Solved n) = n
conquer (Pending a b) = a b
I originally tried to write conquer = sum (with the appropriate Foldable instance), but this is incorrect. We're not summing up the a types in the subproblem; rather, all the interesting values are in the Cent field of the Solved constructor, and sum doesn't look at those because they're not of type a.
Finally, we let recursion schemes do the actual recursion for us with a simple hylo call:
waysToMakeChange :: ChangePuzzleArgs -> Int
waysToMakeChange = hylo conquer divide
And we can confirm it works in GHCI:
*Main> waysToMakeChange (coins, 10)
4
*Main> waysToMakeChange (coins, 100)
292
Whether you think this is worth the effort is up to you. Recursion schemes have saved us very little work here, as this problem is easy to solve by hand. But you may find reifying the intermediate states makes the recursive structure explicit, instead of implicit in the call graph. Anyway it's an interesting exercise if you want to practice recursion schemes in preparation for more complicated tasks.
The full, working file is included below for convenience.
{-# LANGUAGE DeriveFunctor #-}
import Control.Arrow ( (>>>), (<<<) )
newtype Term f = In {out :: f (Term f)}
type Algebra f a = f a -> a
type Coalgebra f a = a -> f a
cata :: (Functor f) => Algebra f a -> Term f -> a
cata fn = out >>> fmap (cata fn) >>> fn
ana :: (Functor f) => Coalgebra f a -> a -> Term f
ana f = In <<< fmap (ana f) <<< f
hylo :: Functor f => Algebra f b -> Coalgebra f a -> a -> b
hylo alg coalg = ana coalg >>> cata alg
data ChangePuzzle a = Solved Cent
| Pending {spend, forget :: a}
deriving Functor
type Cent = Int
type ChangePuzzleArgs = ([Cent], Cent)
coins :: [Cent]
coins = [50, 25, 10, 5, 1]
divide :: Coalgebra ChangePuzzle ChangePuzzleArgs
divide (_, 0) = Solved 1
divide ([], _) = Solved 0
divide (coins@(x:xs), n) | n < 0 = Solved 0
| otherwise = Pending (coins, n - x) (xs, n)
conquer :: Algebra ChangePuzzle Cent
conquer (Solved n) = n
conquer (Pending a b) = a b
waysToMakeChange :: ChangePuzzleArgs -> Int
waysToMakeChange = hylo conquer divide