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        "author": "evopy",
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        "description": "# evopy\n\n[![Build Status](https://travis-ci.com/evopy/evopy.svg?branch=master)](https://travis-ci.com/evopy/evopy)\n[![codecov](https://codecov.io/gh/evopy/evopy/branch/master/graph/badge.svg)](https://codecov.io/gh/evopy/evopy)\n[![PyPI](https://img.shields.io/pypi/v/evopy.svg)](https://pypi.org/project/evopy/)\n[![Docs](https://readthedocs.org/projects/evopy/badge/?version=latest)](http://evopy.readthedocs.io/)\n\n**Evolutionary Strategies made simple!**\n\nUse evopy to easily optimize a vector of floats in Python.\n\n## \ud83c\udfd7 Installation\n\nAll you need to use evopy is [Python 3](https://www.python.org/downloads/)! Run this command to fetch evopy from PyPI:\n\n```\npip install evopy\n```\n\nThen you can import `EvoPy` like this:\n\n```python\nfrom evopy import EvoPy\n```\n\n## \u23e9 Usage\n\n### One-Dimensional Example\n\nLet's say we wanted to find the optimum of a parabola, without using exact methods from calculus! With Evopy, this is as easy as writing the following two lines:\n\n```python\nevopy = EvoPy(lambda x: pow(x, 2), 1)\nbest_coordinates = evopy.run()\n```\n\nThe main ingredient here is the fitness function (the lambda). This can also be a normal function reference, just make sure that it accepts a float or an array of floats and outputs a single float. The other ingredient is the `1` at the end of the first line: This is the dimensionality of the inputs that you expect in your fitness function. `best_coordinates` will contain an array with a single element, which is the best `x` value the algorithm could find in the default number of generations.\n\n### Multi-Dimensional Example\n\nIf the previous example seemed too simple to you, we can also look at the optimum of a more complex, two-dimensional function, like the [Rastrigin function](https://en.wikipedia.org/wiki/Rastrigin_function). We don't have to modify much in our previous code snippet to get this to work:\n\n```python\nevopy = EvoPy(\n    lambda X: 5 + sum([(x**2 - 5 * np.cos(2 * np.pi * x)) for x in X]), \n    2, \n    generations=1000, \n    population_size=100\n)\nbest_coordinates = evopy.run()\n```\n\nCompared to the first example, we have interchanged the fitness function for a more complex one, set the dimensionality to `2`, and given the algorithm more time to find an optimum by setting a higher generation and individual count than the default.\n\n### Docs\n\nFor more detailed information on evopy's functionality, have a look at [the docs](http://evopy.readthedocs.io/)!\n\n## \u26cf Development\n\n[Clone this repository](https://github.com/evopy/evopy) and fetch all dependencies from within the cloned directory:\n\n```\npip install -r requirements.dev.txt\n```\n\nRun all tests with:\n\n```\nnosetests\n```\n\nTo check your code style, run:\n\n```\npylint evopy\n```\n\nTo measure your code coverage, run:\n\n```\nnosetests --with-coverage --cover-package=evopy --cover-html --cover-branches --cover-erase\n```\n\n\n",
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