kmeans 0.1.1 → 0.1.2

raw patch · 2 files changed

+34/−16 lines, 2 filesnew-uploaderPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

API changes (from Hackage documentation)

Files

• Data/KMeans.hs +31/−12
• kmeans.cabal +3/−4

@@ -1,40 +1,59 @@-module Data.KMeans (kmeans, kmeans')+{- |+Module      :  Data.KMeans+Copyright   :  (c) Keegan Carruthers-Smith, 2009+License     :  BSD 3 Clause+Maintainer  :  gershomb@gmail.com+Stability   :  experimental++A simple implementation of the standard k-means clustering algorithm: <http://en.wikipedia.org/wiki/K-means_clustering>. K-means clustering partitions points into clusters, with each point belonging to the cluster with th nearest mean. As the general problem is NP hard, the standard algorithm, which is relatively rapid, is heuristic and not guaranteed to converge to a global optimum. Varying the input order, from which the initial clusters are generated, can yield different results. For degenerate and malicious cases, the algorithm may take exponential time.++-}+module Data.KMeans (kmeans, kmeansGen)     where  import Data.List (transpose, sort, groupBy, minimumBy) import Data.Function (on) import Data.Ord (comparing) -type Vector a = [a]+data WrapType a = WrapType {getVect :: [Double], getVal :: a}+instance Eq (WrapType a) where+   (==) = (==) `on` getVect+instance Ord (WrapType a) where+    compare = comparing getVect  dist a b = sqrt . sum $ zipWith (\x y-> (x-y) ^ 2) a b -centroid points = map (flip (/) l . sum) $ transpose points+centroid points = map (flip (/) l . sum) $ transpose (map getVect points)     where l = fromIntegral $ length points  closest points point = minimumBy (comparing $ dist point) points  recluster' centroids points = map (map snd) $ groupBy ((==) `on` fst) reclustered-    where reclustered = sort [(closest centroids a, a) | a <- points]+    where reclustered = sort [(closest centroids (getVect a), a) | a <- points]  recluster clusters = recluster' centroids $ concat clusters     where centroids = map centroid clusters +part :: (Eq a) => Int -> [a] -> [[a]] part x ys      | zs' == [] = [zs]      | otherwise = zs : part x zs'     where (zs, zs') = splitAt x ys  -- | Recluster points-kmeans' :: (Floating a, Ord a) => [[Vector a]] -> [[Vector a]]-kmeans' clusters+kmeans'' clusters     | clusters == clusters' = clusters-    | otherwise             = kmeans' clusters'+    | otherwise             = kmeans'' clusters'     where clusters' = recluster clusters --- | Cluster points into k clusters.--- |--- | The initial clusters are chosen arbitrarily-kmeans :: (Floating a, Ord a) => Int -> [Vector a] -> [[Vector a]]-kmeans k points = kmeans' $ part l points+kmeans' k points = kmeans'' $ part l points     where l = (length points + k - 1) `div` k++-- | Cluster points in a Euclidian space, represented as lists of Doubles, into at most k clusters.+-- The initial clusters are chosen arbitrarily.+kmeans :: Int -> [[Double]] -> [[[Double]]]+kmeans = kmeansGen id++-- | A generalized kmeans function. This function operates not on points, but an arbitrary type which may be projected into a Euclidian space. Since the projection may be chosen freely, this allows for weighting dimensions to different degrees, etc.+kmeansGen :: (a -> [Double]) -> Int -> [a] -> [[a]]+kmeansGen f k points = map (map getVal) . kmeans' k . map (\x -> WrapType (f x) x) $ points

@@ -1,13 +1,12 @@ Name:               kmeans-Version:            0.1.1-Description:-    A simple library for k-means clustering+Version:            0.1.2+Description:        A simple library for k-means clustering Category:           algorithms, clustering, data mining Synopsis:           K-means clustering algorithm License:            BSD3 License-file:       LICENSE Author:             Keegan Carruthers-Smith-Maintainer:         max.rabkin@gmail.com+Maintainer:         gershomb@gmail.com Stability:          Alpha Build-Type:         Simple Cabal-Version:      >= 1.2