<div dir="ltr">Thanks Mark, this is very helpful. <div><br><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, May 24, 2018 at 6:36 PM, Mark Wotton <span dir="ltr"><<a href="mailto:mwotton@gmail.com" target="_blank">mwotton@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">there are a few things you can do.<br>
<br>
I ran it locally and got 3s cpu time and 3gb of allocation, more or less.<br>
<br>
first off, your subset implementation computes the subsets twice.<br>
taking that off took it down to 2s.<br>
<br>
After that it's mostly in the Set implementation. Using Map a ()<br>
instead gives you access to Data.Map.Merge.Strict.merge, which is a<br>
bit more efficient (going over the two data structures once only)<br>
<br>
```<br>
module Main where<br>
<br>
import Data.List<br>
import Data.Map.Strict (Map)<br>
import qualified Data.Map.Merge.Strict as Map<br>
import qualified Data.Map.Strict as Map<br>
<br>
nearest_k :: (Ord a) => Int -> [(Map a (), v)] -> Map a () -> [(Map a (), v)]<br>
<span class="">nearest_k k bs b = take k bs' where<br>
bs' = sortOn (hamming b) bs<br>
<br>
</span>hamming :: (Ord a)=> Map a () -> (Map a (), v) -> Int<br>
<span class="">hamming x (y, _) = hamming_distance x y<br>
<br>
</span>hamming_distance :: (Ord a)=> Map a () -> Map a () -> Int<br>
hamming_distance xs ys = Map.size (Map.merge Map.preserveMissing<br>
Map.preserveMissing (Map.zipWithMaybeMatched (\_ _ _ -> Nothing)) xs<br>
ys)<br>
<span class=""><br>
subsets :: [a] -> [[a]]<br>
subsets [] = [[]]<br>
</span>subsets (x:xs) = let rs = subsets xs in rs ++ map (x:) rs<br>
<span class=""><br>
int_lists :: [[Int]]<br>
int_lists = subsets [1..20]<br>
<br>
</span>values :: [(Map Int (), Int)]<br>
<span class="">values = map f (zip [1..] int_lists) where<br>
</span> f (i, x) = (Map.fromList(zip x (repeat ())), i)<br>
<br>
test = nearest_k 8 values (Map.fromList (zip [1,2,3] (repeat ())))<br>
<br>
<br>
main = print test<br>
```<br>
<br>
that took it to 1s, and now profiling indicates more than half the<br>
time is spent in generation of the test values, so I'll leave it<br>
there.<br>
<br>
I think if you wanted to do better than this you'd have to do some<br>
algorithmic changes - for instance, once your worst candidate is n<br>
steps away, you can stop calculating the hamming distance for anything<br>
else once it's > n, as it can't contribute usefully to the nearest<br>
neighbours.<br>
<br>
cheers,<br>
Mark<br>
<div class="HOEnZb"><div class="h5"><br>
On Thu, May 24, 2018 at 12:15 PM, Richard Evans<br>
<<a href="mailto:richardprideauxevans@gmail.com">richardprideauxevans@gmail.<wbr>com</a>> wrote:<br>
> Dear Haskell Cafe,<br>
><br>
> Given a set of sets, and a particular target set, I want to find the sets<br>
> that are nearest (in terms of Hamming distance) to the target set.<br>
><br>
> I am using the following code:<br>
><br>
> import Data.List<br>
> import qualified Data.Set as Set<br>
><br>
> nearest_k :: Ord a => Int -> [(Set.Set a, v)] -> Set.Set a -> [(Set.Set a,<br>
> v)]<br>
> nearest_k k bs b = take k bs' where<br>
> bs' = sortOn (hamming b) bs<br>
><br>
> hamming :: Ord a => Set.Set a -> (Set.Set a, v) -> Int<br>
> hamming x (y, _) = hamming_distance x y<br>
><br>
> hamming_distance :: Ord a => Set.Set a -> Set.Set a -> Int<br>
> hamming_distance xs ys = Set.size (Set.difference xs ys) + Set.size<br>
> (Set.difference ys xs)<br>
><br>
><br>
><br>
> subsets :: [a] -> [[a]]<br>
> subsets [] = [[]]<br>
> subsets (x:xs) = subsets xs ++ map (x:) (subsets xs)<br>
><br>
> int_lists :: [[Int]]<br>
> int_lists = subsets [1..20]<br>
><br>
> values :: [(Set.Set Int, Int)]<br>
> values = map f (zip [1..] int_lists) where<br>
> f (i, x) = (Set.fromList x, i)<br>
><br>
> test = nearest_k 8 values (Set.fromList [1,2,3])<br>
><br>
> ----<br>
><br>
> This works ok for the test above (with sets of ints), but is rather slow in<br>
> my actual application (in which the sets are large sets of ground atoms of<br>
> first-order logic). Is there some major optimization I should be doing here?<br>
><br>
> thanks,<br>
> Richard<br>
><br>
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