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        "author": "Teodor Scorpan",
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        "description": "# Clustering with nature inspired algorithms\n\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/5c80f366d3044d1381b852f79d03fd58)](https://app.codacy.com/app/Catastropha/pyriad)\n[![Codacy Badge](https://api.codacy.com/project/badge/coverage/5c80f366d3044d1381b852f79d03fd58)](https://app.codacy.com/app/Catastropha/pyriad)\n[![Build Status](https://api.travis-ci.org/catastropha/pyriad.svg?branch=master)](https://travis-ci.org/catastropha/pyriad)\n[![Version](https://img.shields.io/pypi/v/pyriad.svg?style=flat)](https://pypi.org/project/pyriad/#history)\n[![PyPI downloads](https://img.shields.io/pypi/dm/pyriad.svg?style=flat)](https://pypi.org/project/pyriad/#files)\n![License](https://img.shields.io/pypi/l/pyriad.svg?style=flat)\n\n`pyriad` offers clustering with a variety of nature inspired algorithms built with Python on top of the deep learning library [PyTorch](https://pytorch.org/).\n\nYou can extend `pyriad` according to your own needs. You can implement custom algorithms by extending simple abstract classes.\nPyriad is highly parallelizable and transferable to GPU.\n\n## Algorithms\nAs of today, the following algorithms have been implemented:\n\n-   [x] Particle Swarm Optimization (PSO) [[1]](https://www.cs.tufts.edu/comp/150GA/homeworks/hw3/_reading6%201995%20particle%20swarming.pdf)\n-   [x] Cuckoo Search (CS) [[2]](https://www.cs.tufts.edu/comp/150GA/homeworks/hw3/_reading7%20Cuckoo%20search.pdf)\n-   [x] Grey Wolf Optimization (GWO) [[3]](https://www.researchgate.net/profile/Mohammed_Bakr6/post/how_to_implement_Open_Vechile_Routing_Problem_using_Grey_Wolf_Optimizer/attachment/59d621c66cda7b8083a1b3fa/AS%3A273784001499151%401442286600462/download/GWO_finalVersion.pdf)\n-   [x] Flower Pollination Algorithm (FP) [[4]](https://arxiv.org/abs/1312.5673)\n\n## Installation\n\n1.  Install PyTorch. You can find it here: [PyTorch](https://pytorch.org/)\n2.  `pip install pyriad`\n\n## Examples\n\nYou can find examples in `examples/` directory\n\nYou can also run examples: `python examples/pso_iris.py`\n\nYou might want to `export PYTHONPATH=/path/to/this/directory`\n\n## Contribute\n\n1.  Implement new algorithms\n2.  Improve code design\n3.  Improve comments and readme\n4.  Tests\n\n",
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