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        "author": "Zhi Zhang",
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        "description": "torchcluster\n============\n\n`Documentation <https://torchcluster.readthedocs.io/en/latest/>`__ \\|\n\nTorchcluster is a python package for cluster analysis. The speed of the\nclustering algorithm has been effectively improved with the Pytorch\nbackend. We are also working on test datasets and visualization tools.\nRelated work is coming in the next release.\n\nSystem requirements\n-------------------\n\ntorchcluster should work on\n\n-  all Linux distributions no earlier than Ubuntu 16.04\n-  macOS X\n-  Windows 10\n\ntorchcluster also requires Python 3.5 or later. Python 2 support is\ncoming.\n\nRight now, torchcluster works on `PyTorch <https://pytorch.org/>`__\n0.4.1.\n\nInstallation\n------------\n\nUsing pip\n~~~~~~~~~\n\n::\n\n    pip install torchcluster\n\nUsing anaconda\n~~~~~~~~~~~~~~\n\n::\n\n    conda install -c tczhangzhi torchcluster\n\nHow torchcluster looks like\n---------------------------\n\nDefine a dataset generator and generate a dataset:\n\n::\n\n    from torchcluster.dataset.simple import SimpleDataset\n\n    dataset_factory = SimpleDataset(2, feature=2, sigma=2, device=device)\n    dataset = dataset_factory(100)\n\nConfiguring a clustering algorithm and get your result:\n\n::\n\n    from torchcluster.zoo.spectrum import SpectrumClustering\n\n    cluster = SpectrumClustering(2)\n    result, _ = cluster(dataset)\n\nYou can also cluster your own data sets. The dataset should be a tensor\nof n by m, where n is the number of data points in the dataset and m is\nthe dimension of each data point:\n\n::\n\n    dataset = torch.cat([torch.randn(500,2) + torch.Tensor([-2,-3]), torch.randn(500,2) + torch.Tensor([2,1])])\n\nUse spectral clustering to get the following results:\n\n::\n\n    tensor([0, 0, ..., 1, 1])\n\nLicense\n-------\n\n`MIT <http://opensource.org/licenses/MIT>`__\n\nCopyright (c) 2019-present, Zhang Zhi",
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