Katjas-kd-tree 0.0.1

An implementation of KD trees

KD-tree-implementation

An implementation of kd-search trees with functions to find the nearest neighbor, an operation that would take a long time using linear search on large datasets. That is where kd-search trees come in, since they can exclude a larger part of the dataset at once.
This project was created as a final project for the course CS110/Computation: Solving Problems with algorithms.

Motivation

#Installation guide

Example use case

To find the closest color in a dataset of named colors, we cannot use our usual quick-search methods or binary search-trees, since the data has more than 1 dimension and cannot simply be ordered. Therefore, we can create a tree with k dimensions, where every new level is split along a new dimension, iterating through all of them as often as needed. This allows us to very quickly get an approximation of the nearest neighbor and with slightly more effort find the exact nearest neighbor quicker than with a linear search.
To find code examples of this use case, open example.py #Background Complexity:
A linear search runs with O(n) complexity, since it has to check every value. find_approx_nearest runs with O(log(n)) complexity on average. The find_exact_nearest function will exclude less of the tree at a time, but still run in O(log(n)), just with a higher constant factor.