{- | Module : Data.Set.BKTree Copyright : (c) Josef Svenningsson 2007-2010 (c) Henning Günter 2007 License : BSD-style Maintainer : josef.svenningsson@gmail.com Stability : Alpha quality. Interface may change without notice. Portability : portable Burkhard-Keller trees provide an implementation of sets which apart from the ordinary operations also has an approximate member search, allowing you to search for elements that are of a distance @n@ from the element you are searching for. The distance is determined using a metric on the type of elements. Therefore all elements must implement the 'Metric' type class, rather than the more usual 'Ord'. Useful metrics include the manhattan distance between two points, the Levenshtein edit distance between two strings, the number of edges in the shortest path between two nodes in an undirected graph and the Hamming distance between two binary strings. Any euclidean space also has a metric. However, in this module we use int-valued metrics and that's not compatible with the metrics of euclidean spaces which are real-values. The worst case complexity of many of these operations is quite bad, but the expected behavior varies greatly with the metric. For example, the discrete metric (@distance x y | y == x = 0 | otherwise = 1@) makes BK-trees behave abysmally. The metrics mentioned above should give good performance characteristics. -} module Data.Set.BKTree (-- The main type BKTree -- Metric ,Metric(..) -- ,null,size,empty ,fromList,singleton ,insert ,member,memberDistance ,delete ,union,unions ,elems,elemsDistance ,closest #ifdef DEBUG ,runTests #endif )where import Data.Set.BKTree.Internal import qualified Data.IntMap as M import qualified Data.List as L hiding (null) import Prelude hiding (null) import Data.Array.IArray (Array,array,listArray,(!),assocs) import Data.Array.Unboxed (UArray) #ifdef DEBUG import qualified Prelude import Test.QuickCheck import Text.Printf import System.Exit #endif -- | A type is 'Metric' if is has a function 'distance' which has the following -- properties: -- -- * @'distance' x y >= 0@ -- -- * @'distance' x y == 0@ if and only if @x == y@ -- -- * @'distance' x y == 'distance' y x@ -- -- * @'distance' x z <= 'distance' x y + 'distance' y z@ -- -- All types of elements to 'BKTree' must implement 'Metric'. -- -- This definition of a metric deviates from the mathematical one in that it -- returns an integer instead of a real number. The reason for choosing -- integers is that I wanted to avoid the rather unpredictable rounding -- of floating point numbers. class Eq a => Metric a where distance :: a -> a -> Int instance Metric Int where distance i j = abs (i - j) -- Fishy instance. Maybe I shouldn't have it. -- Or generalize Metric to use integer? instance Metric Integer where distance i j = fromInteger (abs (i - j)) instance Metric Char where distance i j = abs (fromEnum i - fromEnum j) hirschberg :: Eq a => [a] -> [a] -> Int hirschberg xs [] = length xs hirschberg xs ys = let lxs = length xs lys = length ys start_arr :: UArray Int Int start_arr = listArray (1,lys) [1..lys] in (L.foldl' (\arr (i,xi) -> let narr :: UArray Int Int narr = array (1,lys) (snd $ L.mapAccumL (\(s,c) ((j,el),yj) -> let nc = minimum [s + (if xi==yj then 0 else 1) ,el + 1 ,c + 1 ] in ((el,nc),(j,nc))) (i-1,i) (zip (assocs arr) ys) ) in narr ) start_arr (zip [1..] xs))!lys instance Eq a => Metric [a] where distance = hirschberg -- -------- -- BKTrees -- -------- -- | Test if the tree is empty. null :: BKTree a -> Bool null (Empty) = True null (Node _ _ _) = False -- | Size of the tree. size :: BKTree a -> Int size (Empty) = 0 size (Node _ s _) = s -- | The empty tree. empty :: BKTree a empty = Empty -- | The tree with a single element singleton :: a -> BKTree a singleton a = Node a 1 M.empty -- | Inserts an element into the tree. If an element is inserted several times -- it will be stored several times. insert :: Metric a => a -> BKTree a -> BKTree a insert a Empty = Node a 1 M.empty insert a (Node b size map) = Node b (size+1) map' where map' = M.insertWith recurse d (Node a 1 M.empty) map d = distance a b recurse _ tree = insert a tree -- | Checks whether an element is in the tree. member :: Metric a => a -> BKTree a -> Bool member a Empty = False member a (Node b _ map) | d == 0 = True | otherwise = case M.lookup d map of Nothing -> False Just tree -> member a tree where d = distance a b -- | Approximate searching. @'memberDistance' n a tree@ will return true if -- there is an element in @tree@ which has a 'distance' less than or equal to -- @n@ from @a@. memberDistance :: Metric a => Int -> a -> BKTree a -> Bool memberDistance n a Empty = False memberDistance n a (Node b _ map) | d <= n = True | otherwise = any (memberDistance n a) (M.elems subMap) where d = distance a b subMap = case M.split (d-n-1) map of (_,mapRight) -> case M.split (d+n+1) mapRight of (mapCenter,_) -> mapCenter -- | Removes an element from the tree. If an element occurs several times in -- the tree then only one occurrence will be deleted. delete :: Metric a => a -> BKTree a -> BKTree a delete a Empty = Empty delete a t@(Node b _ map) | d == 0 = unions (M.elems map) | otherwise = Node b sz subtrees where d = distance a b subtrees = M.update (Just . delete a) d map sz = sum (L.map size (M.elems subtrees)) + 1 -- | Returns all the elements of the tree elems :: BKTree a -> [a] elems Empty = [] elems (Node a _ imap) = a : concatMap elems (M.elems imap) -- | @'elemsDistance' n a tree@ returns all the elements in @tree@ which are -- at a 'distance' less than or equal to @n@ from the element @a@. elemsDistance :: Metric a => Int -> a -> BKTree a -> [a] elemsDistance n a Empty = [] elemsDistance n a (Node b _ imap) = (if d <= n then (b :) else id) $ concatMap (elemsDistance n a) (M.elems subMap) where d = distance a b subMap = case M.split (d-n-1) imap of (_,mapRight) -> case M.split (d+n+1) mapRight of (mapCenter,_) -> mapCenter -- | Constructs a tree from a list fromList :: Metric a => [a] -> BKTree a fromList xs = L.foldl' (flip insert) empty xs -- | Merges several trees unions :: Metric a => [BKTree a] -> BKTree a unions xs = fromList $ concat $ map elems xs -- | Merges two trees union :: Metric a => BKTree a -> BKTree a -> BKTree a union t1 t2 = unions [t1,t2] -- | @'closest' a tree@ returns the element in @tree@ which is closest to -- @a@ together with the distance. Returns @Nothing@ if the tree is empty. closest :: Metric a => a -> BKTree a -> Maybe (a,Int) closest a Empty = Nothing closest a tree@(Node b _ _) = Just (closeLoop a (b,distance a b) tree) closeLoop a candidate Empty = candidate closeLoop a candidate@(_,d) (Node x _ imap) = L.foldl' (closeLoop a) newCand (M.elems subMap) where newCand = if j >= d then candidate else (x,j) j = distance a x subMap = case M.split (d-j-1) imap of (_,mapRight) -> case M.split (d+j+1) mapRight of (mapCenter,_) -> mapCenter -- Helper functions on rel f x y = rel (f x) (f y) #ifdef DEBUG -- Testing -- N.B. This code requires QuickCheck 2.0 {- Testing using algebraic specification. The idea is that we have this naive inefficient distance function. But instead of comparing it to our actual implementation we take each clause in the definition and make it into an equation. We also change each occurrence of the name naive to a call to the distance function. naive [] ys = length ys naive xs [] = length xs naive (x:xs) (y:ys) | x == y = naive xs ys naive (x:xs) (y:ys) = 1 + minimum [naive (x:xs) ys ,naive (x:xs) (x:ys) ,naive xs (y:ys)] For example, the third clause becomes: distance (x:xs) (x:ys) == distance xs ys That way we can construct a quickCheck property from it. So, one property for each equation in the naive algorithm. Pretty sweet! Credits go to Koen. -} -- Way too inefficient! -- prop_naive xs ys = distance xs ys == naive xs (ys :: [Int]) prop_naiveEmpty xs = distance [] xs == length xs && distance xs [] == length (xs::[Int]) prop_naiveCons x xs ys = distance (x:xs) (x:ys) == distance xs (ys::[Int]) prop_naiveDiff x y xs ys = x /= y ==> distance (x:xs) (y:ys) == 1 + minimum [distance (x:xs) (ys :: [Int]) ,distance (x:xs) (x:ys) ,distance xs (y:ys)] -- ---------------------------------------------------- -- Semantics of BKTrees. Just a boring list of integers sem tree = L.sort (elems tree) :: [Int] -- For testing functions that transform trees trans f xs = sem (f (fromList xs)) invariant t = inv [] t inv dict Empty = True inv dict (Node a _ imap) = all (\ (d,b) -> distance a b == d) dict && all (\ (d,t) -> inv ((d,a):dict) t) (M.toList imap) -- Tests for individual functions prop_empty n = not (member (n::Int) empty) prop_null xs = null (fromList xs) == Prelude.null (xs :: [Int]) prop_singleton n = elems (fromList [n]) == [n :: Int] prop_fromList xs = sem (fromList xs) == L.sort xs prop_fromListInv xs = invariant (fromList (xs :: [Int])) prop_insert n xs = trans (insert n) xs == L.sort (n:xs) prop_insertInv n xs = invariant (insert n (fromList (xs :: [Int]))) prop_member n xs = member n (fromList xs) == L.elem (n::Int) xs prop_memberDistance dist n xs = let d = dist `mod` 5 ref = L.any (\e -> distance n e <= d) xs in collect ref $ memberDistance d n (fromList xs) == L.any (\e -> distance n e <= d) (xs :: [Int]) prop_delete n xs = trans (delete n) xs == L.sort (removeFirst (xs :: [Int])) where removeFirst [] = [] removeFirst (a:as) | a == n = as | otherwise = a : removeFirst as prop_deleteInv n xs = invariant (delete n (fromList (xs :: [Int]))) prop_elems xs = L.sort (elems (fromList xs)) == L.sort (xs::[Int]) prop_elemsDistance dist n xs = let d = dist `mod` 5 in L.sort (elemsDistance d n (fromList xs)) == L.sort (filter (\e -> distance n e <= d) (xs::[Int])) prop_unions xss = sem (unions (map fromList xss)) == L.sort (concat (xss::[[Int]])) prop_unionsInv xss = invariant (unions (map fromList (xss :: [[Int]]))) prop_union xs ys = sem (union (fromList xs) (fromList ys)) == L.sort (xs ++ (ys::[Int])) prop_unionInv xs ys = invariant (union (fromList (xs :: [Int])) (fromList (ys :: [Int]))) -- Error case : 0 [1073741824,0] -- QuickCheck 2.1 finds this easily. -- The above error case hit the limit of Int. -- Maybe I should use Integer after all? prop_closest n xs = -- Some arbitrary level so that we don't hit the limit of Int all (\x -> abs x < 100000) xs ==> case (closest n (fromList xs),xs) of (Nothing,[]) -> True (Just (_,d),ys) -> d == minimum (map (distance n) (ys::[Int])) _ -> False -- Testing the relations between operations prop_insertDelete n xs = trans (delete n . insert n) xs == L.sort (xs::[Int]) prop_sizeEmpty = size empty == 0 prop_sizeFromList xs = size (fromList xs) == length (xs :: [Int]) prop_sizeSucc n xs = size (insert (n::Int) tree) == size tree + 1 where tree = fromList xs prop_sizeDelete n xs = size (delete (n::Int) tree) == size tree - (if n `member` tree then 1 else 0) where tree = fromList xs prop_sizeUnion xs ys = size (union treeXs treeYs) == size treeXs + size treeYs where (treeXs,treeYs) = (fromList xs, fromList (ys :: [Int])) prop_sizeUnions xss = size (unions trees) == sum (map size trees) where trees = map fromList (xss :: [[Int]]) prop_unionsMember xss = all (\x -> member x tree) (concat (xss :: [[Int]])) where tree = unions (map fromList xss) prop_fromListMember xs = all (\x -> member x tree) (xs :: [Int]) where tree = fromList xs -- All the tests data TestCase = forall prop. Testable prop => Tc String prop tests = [Tc "empty" prop_empty ,Tc "null" prop_null ,Tc "singleton" prop_singleton ,Tc "fromList" prop_fromList ,Tc "fromList inv" prop_fromListInv ,Tc "insert" prop_insert ,Tc "insert inv" prop_insertInv ,Tc "member" prop_member ,Tc "memberDistance" prop_memberDistance ,Tc "delete" prop_delete ,Tc "delete inv" prop_deleteInv ,Tc "elems" prop_elems ,Tc "elemsDistance" prop_elemsDistance ,Tc "unions" prop_unions ,Tc "unions inv" prop_unionsInv ,Tc "union" prop_union ,Tc "union inv" prop_unionInv ,Tc "closest" prop_closest ,Tc "size/empty" prop_sizeEmpty ,Tc "size/fromList" prop_sizeFromList ,Tc "size/succ" prop_sizeSucc ,Tc "size/delete" prop_sizeDelete ,Tc "size/union" prop_sizeUnion ,Tc "size/unions" prop_sizeUnions ,Tc "insert/delete" prop_insertDelete ,Tc "fromList/member" prop_fromListMember ,Tc "unions/member" prop_unionsMember ,Tc "naiveEmpty" prop_naiveEmpty ,Tc "naiveCons" prop_naiveCons ,Tc "naiveDiff" prop_naiveDiff ] runTests = mapM_ runTest tests where runTest (Tc s prop) = do printf "%-25s :" s result <- quickCheckResult prop case result of Success _ _ _ -> return () GaveUp _ _ _ -> return () _ -> exitFailure #endif