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        "author": "chankane",
        "author_email": "brawnychocolate@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.7"
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        "description": "# pyind\nA genetic algorithm library in Python3\n\n**pyind ONLY supports ndarray (numpy)**\n\n[\u65e5\u672c\u8a9e](https://github.com/chankane/pyind/blob/master/README.ja.md)\n\n## Installation\n``pip install pyind``\n\n## About evaluation function\nAn evaluation function has the following format\n```python\ndef evaluation_function(individual):  # individual is an array of gene\n    return fitness_of_this_individual\n\n```\n\n## About `conf`\n`conf` has the following format\n```python\nconf_format = {\n    \"eval\": {\n        \"func\": evaluation_function  # Required fields and it has not default value.\n    },\n    \"sel\": {\n        # See \"Table Sel\" below\n    },\n    \"xovr\": {\n        # See \"Table Xovr\" below\n    },\n    \"mut\": {\n        # See \"Table Mut\" below\n    },\n}\n```\nValue that can be set to `conf[\"sel\"]` are as shown in the table\n\nIn parentheses is default value\n\nTable Sel\n\n\"sel\" (elitism) | \"num\" (10)\n-- | :--:\nelitism | 0–size of poplation\nroulette | 0–size of poplation\n\nValue that can be set to `conf[\"xovr\"]` are as shown in the table\n\nIn parentheses is default value\n\nTable Xovr\n\n\"xovr\" (p2) | \"pb\" (0.875)\n-- | :--:\np2 | 0–1\nuniform | 0–1\nox | 0–1\n\nValue that can be set to `conf[\"mut\"]` are as shown in the table\n\nIn parentheses is default value\n\nTable Mut\n\n\"mut\" (flip_bit) | \"pb\" (0.0075)| \"delta\" (1)\n-- | :--: | :--:\nflip_bit | 0–1\nboundary | 0–1 | 0–∞\nswap_idx | 0–1\n\n## Future Releases\n1. Fix bug\n1. Add functions of selection, crossover and mutation\n1. Run more faster\n## License\nMIT\n\n## Sample code\n### Onemax problem\n```python\n# Onemax Problem\nimport numpy as np\n\nfrom pyind import pyind as pi\nfrom pyind import defaults as df\n\n\nIND_LEN = 100\nPOP_LEN = 100\n\n\ndef evl(ind):\n    return ind.sum()\n\n\nif __name__ == \"__main__\":\n    pop = np.random.randint(2, size=(POP_LEN, IND_LEN))\n\n    conf = df.CONF\n    conf[\"eval\"][\"func\"] = evl\n\n    best = pi.Pyind(pop, conf).run()\n\n    print(\"best ind: \")\n    print(best)\n\n```\n### Traveling salesman problem (TSP)\n```python\n# Traveling salesman problem\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom pyind import pyind as pi\nfrom pyind import crossover as xovr\nfrom pyind import mutation as mut\nfrom pyind import defaults as df\n\n\nCITIES_LEN = 30\nPOP_LEN = 300\nEND_GEN = 500\n\ncities = np.random.rand(CITIES_LEN * 2).reshape((-1, 2))\n\n\ndef evl(ind):\n    total = 0\n    for i in range(1, len(ind)):\n        total += np.linalg.norm(cities[ind[i]] - cities[ind[i - 1]])\n    return -total\n\n\ndef solve(pop):\n    conf = df.CONF\n    conf[\"eval\"][\"func\"] = evl\n    conf[\"xovr\"][\"func\"] = xovr.ox\n    conf[\"mut\"][\"func\"] = mut.swap_idx\n    conf[\"mut\"][\"pb\"] = 0.10\n    return pi.Pyind(pop, conf).run(END_GEN)\n\n\nif __name__ == \"__main__\":\n    t = cities.T\n\n    # Create pop\n    pop = np.tile(np.arange(CITIES_LEN), (POP_LEN, 1))\n    for e in pop:\n        np.random.shuffle(e)\n\n    # Plot gen 0\n    idx = pop[0]\n    plt.plot(t[0, idx], t[1, idx], label=\"gen 0\", marker=\"o\")\n\n    best = solve(pop)\n    print(\"best ind: \")\n    print(best)\n\n    # Plot gen END_GEN\n    idx = best\n    plt.plot(t[0, idx], t[1, idx], label=\"gen \" + str(END_GEN), marker=\"o\")\n\n    plt.legend()\n    plt.show()\n\n```\n\n\n",
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