# Class Containers::KDTree

Parent: Object

A kd-tree is a binary tree that allows one to store points (of any space dimension: 2D, 3D, etc). The structure of the resulting tree makes it so that large portions of the tree are pruned during queries.

One very good use of the tree is to allow nearest neighbor searching. Let’s say you have a number of points in 2D space, and you want to find the nearest 2 points from a specific point:

First, put the points into the tree:

```kdtree = Containers::KDTree.new( {0 => [4, 3], 1 => [3, 4], 2 => [-1, 2], 3 => [6, 4],
4 => [3, -5], 5 => [-2, -5] })```

Then, query on the tree:

`puts kd.find_nearest([0, 0], 2) => [[5, 2], [9, 1]]`

The result is an array of [distance, id] pairs. There seems to be a bug in this version.

Note that the point queried on does not have to exist in the tree. However, if it does exist, it will be returned.

## Constants

 Node = Struct.new(:id, :coords, :left, :right)

## Public class methods

new (points)

Points is a hash of id => [coord, coord] pairs.

[show source]
```    # File lib/containers/kd_tree.rb, line 30
30:   def initialize(points)
31:     raise "must pass in a hash" unless points.kind_of?(Hash)
32:     @dimensions = points[ points.keys.first ].size
33:     @root = build_tree(points.to_a)
34:     @nearest = []
35:   end```

## Public instance methods

find_nearest (target, k_nearest)

Find k closest points to given coordinates

[show source]
```    # File lib/containers/kd_tree.rb, line 38
38:   def find_nearest(target, k_nearest)
39:     @nearest = []
40:     nearest(@root, target, k_nearest, 0)
41:   end```