Crates.io | kmeans |
lib.rs | kmeans |
version | 0.10.0 |
source | src |
created_at | 2019-07-27 15:57:05.090304 |
updated_at | 2024-06-21 19:07:47.201938 |
description | Small and fast library for k-means clustering calculations. |
homepage | |
repository | https://github.com/seijikun/kmean-rs |
max_upload_size | |
id | 152102 |
size | 118,468 |
kmeans is a small and fast library for k-means clustering calculations. It requires a nightly compiler with the portable_simd feature to work.
Here is a small example, using kmean++ as initialization method and lloyd as k-means variant:
use kmeans::*;
fn main() {
let (sample_cnt, sample_dims, k, max_iter) = (20000, 200, 4, 100);
// Generate some random data
let mut samples = vec![0.0f64;sample_cnt * sample_dims];
samples.iter_mut().for_each(|v| *v = rand::random());
// Calculate kmeans, using kmean++ as initialization-method
// KMeans<_, 8> specifies to use f64 SIMD vectors with 8 lanes (e.g. AVX512)
let kmean: KMeans<_, 8> = KMeans::new(samples, sample_cnt, sample_dims);
let result = kmean.kmeans_lloyd(k, max_iter, KMeans::init_kmeanplusplus, &KMeansConfig::default());
println!("Centroids: {:?}", result.centroids);
println!("Cluster-Assignments: {:?}", result.assignments);
println!("Error: {}", result.distsum);
}
For performance-reasons, all calculations are done on bare vectors, using hand-written SIMD intrinsics from the packed_simd
crate. All vectors are stored row-major, so each sample is stored in a consecutive block of memory.