From 0ae2515353bab6b2bc17ab07d5bc4a2cc17ef97e Mon Sep 17 00:00:00 2001 From: Sergey Grebenshchikov Date: Sun, 6 Oct 2024 10:31:00 +0200 Subject: [PATCH] README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0cb4c8e..eb33309 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ import "github.com/keilerkonzept/bitknn" ``` -`bitknn` is a fast [k-nearest neighbors (k-NN)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) library for `uint64`s, using Hamming distance to measure similarity. +`bitknn` is a fast [k-nearest neighbors (k-NN)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) library for `uint64`s, using (bitwise) Hamming distance. If you need to classify **binary feature vectors that fit into `uint64`s**, this library might be useful. It is fast mainly because we can use cheap bitwise ops (XOR + POPCNT) to calculate distances between `uint64` values. For smaller datasets, the performance of the [neighbor heap](heap.go) is also relevant, and so this part has been tuned here also.