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https://github.com/subhadarship/kmeans_pytorch
kmeans using PyTorch
https://github.com/subhadarship/kmeans_pytorch
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kmeans using PyTorch
- Host: GitHub
- URL: https://github.com/subhadarship/kmeans_pytorch
- Owner: subhadarship
- License: mit
- Created: 2019-12-25T07:43:23.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-05-09T08:44:31.000Z (over 1 year ago)
- Last Synced: 2024-10-09T10:34:35.236Z (about 1 month ago)
- Topics: docs, github-pages, gpu, jekylbook, jekyll, kmeans-clustering, pytorch
- Language: Jupyter Notebook
- Homepage: https://subhadarship.github.io/kmeans_pytorch
- Size: 1010 KB
- Stars: 473
- Watchers: 7
- Forks: 76
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# K Means using PyTorch
PyTorch implementation of kmeans for utilizing GPU
# Getting Started
```
import torch
import numpy as np
from kmeans_pytorch import kmeans
# data
data_size, dims, num_clusters = 1000, 2, 3
x = np.random.randn(data_size, dims) / 6
x = torch.from_numpy(x)
# kmeans
cluster_ids_x, cluster_centers = kmeans(
X=x, num_clusters=num_clusters, distance='euclidean', device=torch.device('cuda:0')
)
```
see [`example.ipynb`](https://github.com/subhadarship/kmeans_pytorch/blob/master/example.ipynb) for a more elaborate example
# Requirements
* [PyTorch](http://pytorch.org/) version >= 1.0.0
* Python version >= 3.6
# Installation
install with `pip`:
```
pip install kmeans-pytorch
```
**Installing from source**
To install from source and develop locally:
```
git clone https://github.com/subhadarship/kmeans_pytorch
cd kmeans_pytorch
pip install --editable .
```
# CPU vs GPU
see [`cpu_vs_gpu.ipynb`](https://github.com/subhadarship/kmeans_pytorch/blob/master/cpu_vs_gpu.ipynb) for a comparison between CPU and GPU
# Notes
- useful when clustering large number of samples
- utilizes GPU for faster matrix computations
- support euclidean and cosine distances (for now)
# Credits
- This implementation closely follows the style of [this](https://github.com/overshiki/kmeans_pytorch)
- Documentation is done using the awesome theme [jekyllbook](https://github.com/ebetica/jekyllbook)
# License
[MIT](https://github.com/subhadarship/kmeans_pytorch/blob/master/LICENSE)