K-Means++
Fast KMeans++ initialization.
A scikit-learn-compatible KMeans implementation with fast SIMD distance computations and parallel centroid initialization.
fastkmeanspp is a Python package that implements a KMeans clone from
scikit-learn with a faster KMeans++ centroid
initialization. It is designed to be a drop-in replacement for
scikit-learn's KMeans when initialization is the bottleneck.
✨ Features
- Fast KMeans++ initialization: Uses optimized squared-distance computations while selecting candidate centroids.
- Portable SIMD kernels: Uses Google Highway to compile vectorized kernels for supported CPU targets and select the best implementation at runtime.
- Parallel initialization: Computes distance rows in parallel with Highway's thread pool.
- scikit-learn compatibility: Provides familiar
fit,predict,labels_,cluster_centers_, andinertia_interfaces. - FAISS clustering: Uses FAISS for the Lloyd iterations after initialization.
How Google Highway fits in
Google Highway is a C++ library for portable SIMD programming. The distance kernel is written once with Highway's vector operations. Highway then builds target-specific versions for the available instruction sets, such as SSE, AVX2, and AVX-512 on x86 CPUs. A small runtime dispatch layer selects the strongest target supported by the current processor and keeps a scalar fallback for portability.
The distance kernel loads several feature values at a time, subtracts the corresponding centroid values, and uses fused multiply-add operations to build the squared distance. Feature dimensions that do not fill a complete vector are handled by a short scalar tail.
During KMeans++ initialization, Highway's thread pool divides independent data rows between workers. The initialized centroids are then handed to FAISS for the Lloyd iterations.
🚀 Installation
python -m pip install fastkmeanspp
🔧 Usage
import numpy as np
from fastkmeanspp import KMeans
X = np.array([[0.0, 0.0], [0.1, 0.2], [4.0, 4.0], [4.2, 3.9]])
model = KMeans(n_clusters=2, random_state=42)
model.fit(X)
print(model.labels_)
print(model.cluster_centers_)
Set n_jobs=1 for serial centroid initialization or n_jobs=-1 to use all
available threads.
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