@@ -31,6 +31,7 @@ Done so far: * Splay Trees Containers::SplayTreeMap, Containers::CSplayTreeMap (C extension), Containers::RubySplayTreeMap * Tries Containers::Trie * Suffix Array Containers::SuffixArray + * kd Tree Containers::KDTree * Search algorithms - Binary Search Algorithms::Search.binary_search README.markdown @@ -29,6 +29,7 @@ * Splay Trees - Containers::SplayTreeMap * Tries - Containers::Trie * Suffix Array - Containers::SuffixArray + * kd Tree - Containers::KDTree * Search algorithms - Binary Search - Algorithms::Search.binary_search lib/algorithms.rb @@ -1,7 +1,22 @@ =begin rdoc - A kd-tree allows searching of points in multi-dimensional space, increasing - efficiency for nearest-neighbor searching in particular. + 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: + + kd = Containers::KDTree.new([ [4, 3], [3, 4], [-1, 2], [6, 4], [3, -5], [-2, -5] ]) + + Then, query on the tree: + + puts kd.find_nearest([0, 0], 2) => [[0, 6], [0, 3]] + + Note that the point queried on does not have to exist in the tree. However, if it does exist, + it will be returned. =end @@ -48,6 +63,7 @@ class Containers::KDTree end nearest end + private :check_nearest # Find k closest points to given coordinates def find_nearest(target, k_nearest) lib/containers/kd_tree.rb 856a42158719799f256e909dc439a52e1716d11b Kanwei Li kanwei@gmail.com http://github.com/kanwei/algorithms/commit/013751f3c5c89679f4bce530cb8b303b5a3633a9 013751f3c5c89679f4bce530cb8b303b5a3633a9 2009-04-03T20:36:39-07:00 2009-04-03T20:36:39-07:00 KDTree docs 11bd0de67a3f7f47baf0961ba1d617e8f5588359 Kanwei Li kanwei@gmail.com