hgeometry-0.10.0.0: src/Algorithms/Geometry/ClosestPair/Naive.hs
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-- |
-- Module : Algorithms.Geometry.ClosestPair.Naive
-- Copyright : (C) Frank Staals
-- License : see the LICENSE file
-- Maintainer : Frank Staals
--
-- Naive \O(n\^2)\) time algorithm to compute the closest pair of points among
-- \(n\) points in \(\mathbb{R}^d\).
--
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module Algorithms.Geometry.ClosestPair.Naive( closestPair
, closestPairWith
, DistanceFunction
) where
import Control.Lens ((^.),_1)
import Data.Ext
import qualified Data.Foldable as F
import Data.Geometry.Point
import Data.Geometry.Properties (NumType)
import Data.Geometry.Vector (Arity)
import Data.LSeq (LSeq)
import qualified Data.List.NonEmpty as NonEmpty
import Data.Semigroup
import Data.Util
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-- | Naive algorithm to compute the closest pair according to the
-- (squared) Euclidean distance in \(d\) dimensions. Note that we need
-- at least two elements for there to be a closest pair.
--
-- running time: \(O(dn^2)\) time.
closestPair :: ( Ord r, Arity d, Num r)
=> LSeq 2 (Point d r :+ p) -> Two (Point d r :+ p)
closestPair = (^._1) . closestPairWith (\p q -> squaredEuclideanDist (p^.core) (q^.core))
type DistanceFunction g = g -> g -> NumType g
-- | Naive algorithm to compute the closest pair of points (and the
-- distance realized by those points) given a distance function. Note
-- that we need at least two elements for there to be a closest pair.
--
-- running time: \(O(T(d)n^2)\), where \(T(d)\) is the time required
-- to evaluate the distance between two points in \(\mathbb{R}^d\).
closestPairWith :: Ord r
=> DistanceFunction (Point d r :+ p)
-> LSeq 2 (Point d r :+ p) -> SP (Two (Point d r :+ p)) r
closestPairWith d = getVal . getMin . sconcat . fmap mkPair . pairs
where
getVal (Arg dist x) = SP x dist
mkPair (Two p q) = Min (Arg (d p q) (Two p q))
-- | Produce all lists from a vec of elements. Since the Vec contains at least two
-- elements, the resulting list is non-empty
pairs :: LSeq 2 a -> NonEmpty.NonEmpty (Two a)
pairs = NonEmpty.fromList . uniquePairs . F.toList