Algorithm::KDimensionalTree

K-dimensional Tree (K-d tree) algorithm implementations.

Algorithm::KDimensionalTree

Raku package with implementations of the K-Dimensional Tree (K-D Tree) algorithm.

Remark: This package should not be confused with "Algorithm::KdTree", [ITp1], which provides Raku bindings to a C-implementation of the K-D Tree algorithm. (A primary motivation for making this package, "Algorithm::KDimensionalTree", was to have a pure-Raku implementation.)

Installation

From Zef ecosystem:

zef install Algorithm::KDimensionalTree

From GitHub:

zef install https://github.com/antononcube/Raku-Algorithm-KDimensionalTree.git

Usage examples

Setup

use Algorithm::KDimensionalTree;
use Data::TypeSystem;
use Text::Plot;
# (Any)

Set of points

Make a random set of points:

my @points = ([(^100).rand, (^100).rand] xx 30).unique;
deduce-type(@points);
# Vector(Vector(Atom((Numeric)), 2), 30)

Create the K-dimensional tree object

my $kdTree = Algorithm::KDimensionalTree.new(@points);

say $kdTree;
# KDTree(points => 30, distance-function => Unknown)

Nearest k-neighbors

Use as a search point one from the points set:

my @searchPoint = |@points.head;
# [47.33064406506299 76.37307064050573]

Find 6 nearest neighbors:

my @res = $kdTree.nearest(@searchPoint, 6);
.say for @res;
# [47.33064406506299 76.37307064050573]
# [33.321989165943265 72.2106395557012]
# [42.367775385271564 92.76001291548755]
# [56.24834629183131 60.79449358393906]
# [44.09972101660911 95.32962593733126]
# [69.96118735293926 66.53535849892195]

Plot

Plot the points, the found nearest neighbors, and the search point:

my @point-char =  <* ⏺ ▲>;
say <data nns search> Z=> @point-char;
say text-list-plot(
[@points, @res, [@searchPoint,]],
:@point-char,
x-limit => (0, 100),
y-limit => (0, 100),
width => 60,
height => 20);
# (data => * nns => ⏺ search => ▲)
# ++----------+-----------+----------+-----------+----------++
# +                                                      *   + 100.00
# |                        ⏺⏺             *                  |
# |                                                          |
# + *                                                        +  80.00
# |                           ▲              *    *          |
# | *        *        ⏺                                      |
# |                                        ⏺          *      |
# +                                ⏺                  **     +  60.00
# |                 *                                        |
# |             *                                          * |
# + *             *                                          +  40.00
# |                            *       **                    |
# |                                                          |
# |                                                          |
# +                                               *          +  20.00
# |                                *         *         *     |
# |             *                                            |
# +                        *                                 +   0.00
# ++----------+-----------+----------+-----------+----------++
#  0.00       20.00       40.00      60.00       80.00      100.00

TODO

  • TODO Implementation

    • TODO Using distance functions other than Euclidean distance

    • TODO Using distance functions from an "universal" package

      • E.g. "Math::DistanceFunctions"

    • TODO Returning properties

      • DONE Points

      • TODO Indexes

      • TODO Labels

      • TODO Distances

      • TODO Combinations of those

    • TODO Having an umbrella function nearest

      • Instead of creating an KDTree object etc.

      • This might require making a functor nearest-function

      • This is better done in a different package

  • TODO Documentation

    • DONE Basic usage examples with text plots

    • TODO More extensive documentation with a Jupyter notebook

      • Using "JavaScript::D3".

    • TODO Corresponding blog post

    • MAYBE Corresponding video

References

[AAp1] Anton Antonov, Data::TypeSystem Raku package, (2023), GitHub/antononcube.

[AAp2] Anton Antonov, Text::Plot Raku package, (2022), GitHub/antononcube.

[ITp1] Itsuki Toyota, Algorithm::KdTree Raku package, (2016-2024), GitHub/titsuki.

Algorithm::KDimensionalTree v0.0.2

K-dimensional Tree (K-d tree) algorithm implementations.

Authors

  • Anton Antonov

License

Artistic-2.0

Dependencies

Test Dependencies

Provides

  • Algorithm::KDimensionalTree
  • Algorithm::NodalKDTree

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