The KdTree package

[ Tags: bsd3, data-mining, data-structures, graphics, library, machine-learning ] [ Propose Tags ]

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

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Versions 0.1, 0.2, 0.2.1,,,
Dependencies base (<5), QuickCheck [details]
License BSD3
Copyright Copyright 2011, Issac Trotts & Contributors
Author Issac Trotts
Category Data Mining, Data Structures, Graphics, Machine Learning
Home page
Source repository head: git clone
Uploaded Fri Sep 29 05:51:50 UTC 2017 by jessekempf
Distributions NixOS:0.2.1
Downloads 1617 total (117 in the last 30 days)
Rating 0.0 (0 ratings) [clear rating]
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Status Docs available [build log]
Last success reported on 2017-09-29 [all 1 reports]
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Readme for KdTree-

[back to package description]
This is a simple library for k-d trees in Haskell, based on the algorithms

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:  :? 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}