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            "License :: OSI Approved :: Apache Software License",
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        "description": "# seriate\nOptimal ordering of elements in a set given their distance matrix.\n\n[![Travis build status](https://travis-ci.com/src-d/seriate.svg?branch=master)](https://travis-ci.com/src-d/seriate)\n[![Code coverage](https://codecov.io/github/src-d/seriate/coverage.svg)](https://codecov.io/github/src-d/seriate)\n[![PyPi package status](https://img.shields.io/pypi/v/seriate.svg)](https://pypi.python.org/pypi/seriate)\n![stability: stable](https://svg-badge.appspot.com/badge/stability/stable?color=007ec6)\n[![Apache 2.0 license](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\n![example](doc/example.png)\n\n[Overview](#overview) \u2022 [How To Use](#how-to-use) \u2022 [Contributions](#contributions) \u2022 [License](#license)\n\n## Overview\n\nThis is a Python implementation of [Seriation](http://nicolas.kruchten.com/content/2018/02/seriation/)\nalgorithm. Seriation is an approach for ordering elements in a set so that the\nsum of the sequential pairwise distances is minimal. We state this task\nas a Travelling Salesman Problem (TSP) and leverage the powerful [Google's or-tools](https://github.com/google/or-tools)\nto do heavy-lifting. Since TSP is NP-hard, it is not possible to calculate\nthe precise solution for a big number of elements. However, the or-tools'\nheuristics work very well in practice, and they are used in e.g. Google Maps.\n\nAny [`numpy.roll`-ed](https://docs.scipy.org/doc/numpy-1.16.0/reference/generated/numpy.roll.html)\nresult is equivalent.\n\n## How To Use\n\n```python\nimport numpy\nfrom scipy.spatial.distance import pdist\nfrom seriate import seriate\n\nelements = numpy.array([\n    [3, 3, 3],\n    [5, 5, 5],\n    [4, 4, 4],\n    [2, 2, 2],\n    [1, 1, 1]\n])\n\nprint(seriate(pdist(elements)))\n\n# Output: [4, 3, 0, 2, 1]\n```\n\nThe example above shows how we order 5 elements: `[3, 3, 3]`,\n`[5, 5, 5]`, `[4, 4, 4]`, `[2, 2, 2]` and `[1, 1, 1]`. The result\nis expected:\n\n1. `[1, 1, 1]` \n2. `[2, 2, 2]` \n3. `[3, 3, 3]` \n4. `[4, 4, 4]` \n5. `[5, 5, 5]`\n\n`pdist` from [`scipy.spatial.distance`](https://docs.scipy.org/doc/scipy/reference/spatial.distance.html)\nuses Euclidean (L2) dstance metric by default, so the distance between\n`[x, x, x]` and `[x + 1, x + 1, x + 1]` is constant: \u221a3. Any other distance\nis bigger, so the optimal ordering is to list our elements in the increasing\nnorm order.\n\n## Contributions\n\nContributions are very welcome and desired! Please follow the [code of conduct](doc/code_of_conduct.md)\nand read the [contribution guidelines](doc/contributing.md).\n\n## License\n\nApache-2.0, see [LICENSE.md](LICENSE.md).\n\n\n",
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