Kd::Tree

Crystal implementation of "K-Dimensional Tree" and "N-Nearest Neighbors" based on http://en.wikipedia.org/wiki/Kd-tree.

Installation

Add this to your application's shard.yml:

dependencies:
  kd_tree:
    github: geocrystal/kd_tree

Usage

require "kd_tree"

Construct a new tree. Each point should be of the form [x, y], where x and y are numbers(Int32, Float64, etc):

kd = Kd::Tree(Int32).new(points)

Find the nearest point to [x, y]. Returns an array with one point:

kd.nearest([x, y])

Find the nearest k points to [x, y]. Returns an array of points:

kd.nearest([x, y], k)

Example

require "kd_tree"

points = [
  [2.0, 3.0],
  [5.0, 4.0],
  [4.0, 7.0],
  [7.0, 2.0],
  [8.0, 1.0],
  [9.0, 6.0],
]

kd = Kd::Tree(Float64).new(points)

kd.nearest([1.0, 1.0])
# => [[2.0, 3.0]])

kd_tree.nearest([1.0, 1.0], 2)
# => [[2.0, 3.0], [5.0, 4.0]])

Performance

Using a tree with 1 million points [x, y] of Float64 on my i7-8550U CPU @ 1.80GHz:

crystal run benchmark/benchmark.cr --release

Benchmarking KD-Tree with 1 million points
build(init): 4.34 seconds
                        user     system      total        real
nearest point   1   0.000017   0.000001   0.000018 (  0.000017)
nearest point   5   0.000022   0.000000   0.000022 (  0.000022)
nearest point  10   0.000021   0.000001   0.000022 (  0.000022)
nearest point  50   0.000058   0.000001   0.000059 (  0.000059)
nearest point 100   0.000087   0.000002   0.000089 (  0.000089)
nearest point 255   0.000248   0.000005   0.000253 (  0.000254)
nearest point 999   0.001033   0.000020   0.001053 (  0.001055)

Contributing

1. Fork it (https://github.com/geocrystal/kd_tree/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Contributors

• mamantoha Anton Maminov - creator, maintainer