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https://badge.fury.io/py/torch-scatter.svg\n[pypi-url]: https://pypi.python.org/pypi/torch-scatter\n[build-image]: https://travis-ci.org/rusty1s/pytorch_scatter.svg?branch=master\n[build-url]: https://travis-ci.org/rusty1s/pytorch_scatter\n[docs-image]: https://readthedocs.org/projects/pytorch-scatter/badge/?version=latest\n[docs-url]: https://pytorch-scatter.readthedocs.io/en/latest/?badge=latest\n[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_scatter/branch/master/graph/badge.svg\n[coverage-url]: https://codecov.io/github/rusty1s/pytorch_scatter?branch=master\n\n# PyTorch Scatter\n\n[![PyPI Version][pypi-image]][pypi-url]\n[![Build Status][build-image]][build-url]\n[![Docs Status][docs-image]][docs-url]\n[![Code Coverage][coverage-image]][coverage-url]\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/rusty1s/pytorch_scatter/master/docs/source/_figures/add.svg?sanitize=true\" /\u003e\n\u003c/p\u003e\n\n--------------------------------------------------------------------------------\n\n**[Documentation](https://pytorch-scatter.readthedocs.io)**\n\nThis package consists of a small extension library of highly optimized sparse update (scatter) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.\nScatter operations can be roughly described as reduce operations based on a given \"group-index\" tensor.\nThe package consists of the following operations:\n\n* [**Scatter Add**](https://pytorch-scatter.readthedocs.io/en/latest/functions/add.html)\n* [**Scatter Sub**](https://pytorch-scatter.readthedocs.io/en/latest/functions/sub.html)\n* [**Scatter Mul**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mul.html)\n* [**Scatter Div**](https://pytorch-scatter.readthedocs.io/en/latest/functions/div.html)\n* [**Scatter Mean**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mean.html)\n* [**Scatter Std**](https://pytorch-scatter.readthedocs.io/en/latest/functions/std.html)\n* [**Scatter Min**](https://pytorch-scatter.readthedocs.io/en/latest/functions/min.html)\n* [**Scatter Max**](https://pytorch-scatter.readthedocs.io/en/latest/functions/max.html)\n\nAll included operations are broadcastable, work on varying data types, and are implemented both for CPU and GPU with corresponding backward implementations.\n\n## Installation\n\nEnsure that at least PyTorch 1.1.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:\n\n```\n$ python -c \"import torch; print(torch.__version__)\"\n\u003e\u003e\u003e 1.1.0\n\n$ echo $PATH\n\u003e\u003e\u003e /usr/local/cuda/bin:...\n\n$ echo $CPATH\n\u003e\u003e\u003e /usr/local/cuda/include:...\n```\n\nThen run:\n\n```\npip install torch-scatter\n```\n\nIf you are running into any installation problems, please create an [issue](https://github.com/rusty1s/pytorch_scatter/issues).\nBe sure to import `torch` first before using this package to resolve symbols the dynamic linker must see.\n\n## Example\n\n```py\nimport torch\nfrom torch_scatter import scatter_max\n\nsrc = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])\nindex = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])\n\nout, argmax = scatter_max(src, index, fill_value=0)\n```\n\n```\nprint(out)\ntensor([[ 0,  0,  4,  3,  2,  0],\n        [ 2,  4,  3,  0,  0,  0]])\n\nprint(argmax)\ntensor([[-1, -1,  3,  4,  0,  1]\n        [ 1,  4,  3, -1, -1, -1]])\n```\n\n## Running tests\n\n```\npython setup.py test\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftchaton%2Ftsd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftchaton%2Ftsd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftchaton%2Ftsd/lists"}