Balanced kmeans with cuda support in Pytorch.
Balanced K-Means clustering in PyTorch
Balanced K-Means clustering in Pytorch with strong GPU acceleration.
Disclaimer: This project is heavily inspired by the project kmeans_pytorch. Each part of the original implementation is combined with the appropriate attribution.
As easy as:
pip install balanced_kmeans
First things first: Classical kmeans algorithm as easy as
from balanced_kmeans import kmeans # experiment constants N = 10000 batch_size = 10 num_clusters = 100 device = 'cuda' cluster_size = N // num_clusters X = torch.rand(batch_size, N, dim, device=device) choices, centers = kmeans(X, num_clusters=num_clusters)
Now, if you want balanced kmeans you can run:
from balanced_kmeans import kmeans_equal N = 10000 batch_size = 10 num_clusters = 100 device = 'cuda' cluster_size = N // num_clusters X = torch.rand(batch_size, N, dim, device=device) choices, centers = kmeans_equal(X, num_clusters=num_clusters)
By default, forge initialization scheme is used for initial cluster centers.
However, you may change the initial cluster centers by providing the keyword
initial_state to either
This is a pet project, so feel free to contribute if you want to add any extra feature. For any bugs, please open a detailed issue.
This implementation extends the package
kmeans_pytorch which contains the
implementation of the original Lloyd's K-means algorithm in Pytorch. You can check (and star!)
the original package here.
For licensing of this project, please refer to this repo as well as the
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size balanced_kmeans-0.0.5.tar.gz (4.5 kB)||File type Source||Python version None||Upload date||Hashes View|