Crystal implementation of "K-Dimensional Tree" and "N-Nearest Neighbors" based on http://en.wikipedia.org/wiki/Kd-tree.
Add this to your application's shard.yml:
dependencies:
kd_tree:
github: geocrystal/kd_treerequire "kd_tree"For example, construct a new tree where each point is represented as a two-dimensional array in the form [x, y], where x and y are numbers (such as Int32, Float64, etc).
kd = Kd::Tree(Array(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)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(Array(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]])Kd::Tree(T) can accept any object that responds to #size and #[](i : Int) methods.
class GeoLocation
property name : String
property longitude : Float64
property latitude : Float64
getter size = 2 # Assuming all GeoLocation objects are 2-dimensional
def initialize(@name : String, @longitude : Float64, @latitude : Float64)
end
# Define an indexer to allow easy access by index for longitude and latitude
def [](index : Int32) : Float64
case index
when 0 then @longitude
when 1 then @latitude
else raise "Index out of bounds"
end
end
end
# Create an array of GeoLocation points
points = [
GeoLocation.new("New York", -73.935242, 40.730610),
GeoLocation.new("Los Angeles", -118.243683, 34.052235),
GeoLocation.new("London", -0.127647, 51.507322),
GeoLocation.new("Tokyo", 139.691711, 35.689487),
]
# Initialize the KD-tree with these points
kd_tree = Kd::Tree(GeoLocation).new(points)
# Find the nearest point to London
target = GeoLocation.new("Near London", -0.125740, 51.508530)
nearest_point = kd_tree.nearest(target, 1)
puts "Nearest to London: #{nearest_point.first.name} (longitude #{nearest_point.first.longitude}, latitude #{nearest_point.first.latitude})"
# Nearest to London: London (longitude -0.127647, latitude 51.507322)For distance calculations, the squared Euclidean distance is used. However, you can easily monkey-patch the Kd::Tree#distance method to implement another algorithm, such as the Haversine formula, to calculate distances between two points given their latitudes and longitudes.
require "haversine"
module Kd
class Tree(T)
private def distance(m : T, n : T)
# Calling `Haversine.distance` with 2 pairs of latitude/longitude coordinates.
# Returns a distance in meters.
Haversine.distance({m.latitude, m.longitude}, {n.latitude, n.longitude}).to_meters
end
end
end
points = [
GeoLocation.new("New York", -73.935242, 40.730610),
GeoLocation.new("Los Angeles", -118.243683, 34.052235),
GeoLocation.new("London", -0.127647, 51.507322),
GeoLocation.new("Tokyo", 139.691711, 35.689487),
]
kd_tree = Kd::Tree(GeoLocation).new(points)
# Find the nearest point to London
target = GeoLocation.new("Near London", -0.125740, 51.508530)
nearest_point = kd_tree.nearest(target, 1)
puts "Nearest to London: #{nearest_point.first.name} (longitude #{nearest_point.first.longitude}, latitude #{nearest_point.first.latitude})"
# Nearest to London: London (longitude -0.127647, latitude 51.507322)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): 3.43 seconds
user system total real
nearest point 1 0.000021 0.000000 0.000021 ( 0.000022)
nearest point 5 0.000014 0.000000 0.000014 ( 0.000014)
nearest point 10 0.000011 0.000000 0.000011 ( 0.000012)
nearest point 50 0.000061 0.000000 0.000061 ( 0.000061)
nearest point 100 0.000083 0.000001 0.000084 ( 0.000084)
nearest point 255 0.000238 0.000002 0.000240 ( 0.000240)
nearest point 999 0.000981 0.000009 0.000990 ( 0.000990)- Fork it (https://github.com/geocrystal/kd_tree/fork)
- Create your feature branch (
git checkout -b my-new-feature) - Commit your changes (
git commit -am 'Add some feature') - Push to the branch (
git push origin my-new-feature) - Create a new Pull Request
- mamantoha Anton Maminov - creator, maintainer