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        "description": "[![Travis](https://api.travis-ci.com/zhaofeng-shu33/pspartition-python-binding.svg?branch=master)](https://travis-ci.com/zhaofeng-shu33/pspartition-python-binding)\n[![Appveyor](https://ci.appveyor.com/api/projects/status/github/zhaofeng-shu33/pspartition-python-binding?branch=master&svg=true)](https://ci.appveyor.com/project/zhaofeng-shu33/pspartition-python-binding)\n\n# Python binding\n[![PyPI](https://img.shields.io/pypi/v/pspartition.svg)](https://pypi.org/project/pspartition)\n## How to build\nThe binding uses `Cython`. \nTo package the library, use `python setup.py bdist_wheel`.\nInstall the package by `pip install --user pspartition`. \nBelow is the pre-built binary packages:\n\n| Platform | py3.6 | py3.7 |\n| -------- | :---: | :---: |\n| Windows  |   T   |   T   |\n| MacOS    |   T   |   T   |\n| Linux    |   T   |   T   |\n\n## Demo code\n![](./example/example.png)\n\n```Python\nimport pspartition # classify the three data points shown in the above figure\ng = pspartion.PsPartition(3, [(0,1,1),(1,2,1),(0,2,5)]) # index started from zero, similarity is 5 for vertex 0 and 2\ng.run() # default to use psp_i algorithm to classify them\nprint(g.get_critical_values()) # [2,5]\nprint(g.get_partitions()) # get the result which has at least 2 categories, which is [0,1,0]\n```\n\n\n\n\n## ChangeLog\n\n- Version 0.2: expose `PSP` (C++) class, which is high customizable in python.\n- Version 0.3: expose `PyGraphPDT` (C++) class, which has similar API as `PyGraph` but different inner implementation.\n- Version 0.5: expose `run_psp_i`  for `InfoCluster`.\n- Version 0.7: change the python binding name from `info_cluster` to `pspartition`.\n\n\n",
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