From 71744aa0ea9b148e68828e952ab244392f040b49 Mon Sep 17 00:00:00 2001 From: Sergey Grebenshchikov Date: Fri, 11 Oct 2024 03:26:03 +0200 Subject: [PATCH] README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 12542ec..fb50954 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ 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 (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. +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](internal/heap/heap.go) is also relevant, and so this part has been tuned here also. If your vectors are **longer than 64 bits**, you can [pack](#packing-wide-data) them into `[]uint64` and classify them using the ["wide" model variants](#packing-wide-data).