Metadata-Version: 1.0
Name: torchcluster
Version: 0.1.3
Summary: Torchcluster is a python package for cluster analysis.
Home-page: https://github.com/tczhangzhi/cluster
Author: Zhi Zhang
Author-email: 850734033@qq.com
License: MIT
Description: torchcluster
        ============
        
        `Documentation <https://torchcluster.readthedocs.io/en/latest/>`__ \|
        
        Torchcluster is a python package for cluster analysis. The speed of the
        clustering algorithm has been effectively improved with the Pytorch
        backend. We are also working on test datasets and visualization tools.
        Related work is coming in the next release.
        
        System requirements
        -------------------
        
        torchcluster should work on
        
        -  all Linux distributions no earlier than Ubuntu 16.04
        -  macOS X
        -  Windows 10
        
        torchcluster also requires Python 3.5 or later. Python 2 support is
        coming.
        
        Right now, torchcluster works on `PyTorch <https://pytorch.org/>`__
        0.4.1.
        
        Installation
        ------------
        
        Using pip
        ~~~~~~~~~
        
        ::
        
            pip install torchcluster
        
        Using anaconda
        ~~~~~~~~~~~~~~
        
        ::
        
            conda install -c tczhangzhi torchcluster
        
        How torchcluster looks like
        ---------------------------
        
        Define a dataset generator and generate a dataset:
        
        ::
        
            from torchcluster.dataset.simple import SimpleDataset
        
            dataset_factory = SimpleDataset(2, feature=2, sigma=2, device=device)
            dataset = dataset_factory(100)
        
        Configuring a clustering algorithm and get your result:
        
        ::
        
            from torchcluster.zoo.spectrum import SpectrumClustering
        
            cluster = SpectrumClustering(2)
            result, _ = cluster(dataset)
        
        You can also cluster your own data sets. The dataset should be a tensor
        of n by m, where n is the number of data points in the dataset and m is
        the dimension of each data point:
        
        ::
        
            dataset = torch.cat([torch.randn(500,2) + torch.Tensor([-2,-3]), torch.randn(500,2) + torch.Tensor([2,1])])
        
        Use spectral clustering to get the following results:
        
        ::
        
            tensor([0, 0, ..., 1, 1])
        
        License
        -------
        
        `MIT <http://opensource.org/licenses/MIT>`__
        
        Copyright (c) 2019-present, Zhang Zhi
Keywords: pytorch,cluster
Platform: UNKNOWN
