Metadata-Version: 2.1
Name: kmeans-pytorch
Version: 0.2
Summary: UNKNOWN
Home-page: https://github.com/subhadarship/kmeans_pytorch
Author: Subhadarshi Panda
Author-email: subhadarshipanda08@gmail.com
License: License :: OSI Approved :: MIT License
Description: # 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 .
        ```
        
        # Notes
        - useful when clustering large number of samples
        - utilizes GPU for faster matrix computations
        - support euclidean and cosine distances (for now)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Requires-Python: >=3.6
Description-Content-Type: text/markdown
