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KdTree: KdTree, for efficient search in K-dimensional point clouds.

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This is a simple library for k-d trees in Haskell. It enables efficient searching through collections of points in O(log N) time on average, using the nearestNeighbor function.

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Versions [RSS] 0.1, 0.2, 0.2.1, 0.2.1.1, 0.2.2.0, 0.2.2.1
Dependencies base (<5), QuickCheck [details]
License BSD-3-Clause
Copyright Copyright 2011, Issac Trotts
Author Issac Trotts
Maintainer issac.trotts@gmail.com
Category Graphics
Home page https://github.com/ijt/kdtree
Source repo head: git clone git@github.com:ijt/kdtree.git
Uploaded by IssacTrotts at 2011-06-27T08:31:39Z
Distributions NixOS:0.2.2.1
Reverse Dependencies 1 direct, 0 indirect [details]
Executables KdTreeTest
Downloads 6050 total (21 in the last 30 days)
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Status Docs uploaded by user
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Readme for KdTree-0.2

[back to package description] 
This is a simple library for k-d trees in Haskell, based on the algorithms
at http://en.wikipedia.org/wiki/K-d_tree.

It enables efficient searching through collections of points in O(log N) time
for randomly distributed points, using the nearestNeighbor function.

Here is an example of an interactive session using this module:

[ ~/haskell/KdTree ] ghci
GHCi, version 7.0.3: http://www.haskell.org/ghc/  :? for help
...
Prelude> :m Data.Trees.KdTree 
Prelude Data.Trees.KdTree> import Test.QuickCheck
Prelude Data.Trees.KdTree Test.QuickCheck> points <- sample' arbitrary :: IO [Point3d]
...
Prelude Data.Trees.KdTree Test.QuickCheck> let tree = fromList points
Prelude Data.Trees.KdTree Test.QuickCheck> nearestNeighbor tree (head points)
Just (Point3d {p3x = 0.0, p3y = 0.0, p3z = 0.0})
Prelude Data.Trees.KdTree Test.QuickCheck> head points
Point3d {p3x = 0.0, p3y = 0.0, p3z = 0.0}