Introduction - If you have any usage issues, please Google them yourself
This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.
The code was initially implemented through vectorization. After discussions with John D Errico, I realized that my algorithm will suffer numerical accurancy problem for data with large values. Then, after trying several approaches, I found simple loops with JIT acceleration is the most efficient solution. Now, the performance of the code is comparable with kd-tree even the latter is coded in a mex file.
The code is very simple, hence is also suitable for beginner to learn knn search.