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        "author": "Genevieve Hayes",
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        "classifiers": [
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# mlrose: Machine Learning, Randomized Optimization and SEarch\nmlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.\n\n## Project Background\nmlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.\n\nIt includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems. \n\nAt the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.\n\n## Main Features\n\n#### *Randomized Optimization Algorithms*\n- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;\n- Solve both maximization and minimization problems;\n- Define the algorithm's initial state or start from a random state;\n- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.\n\n#### *Problem Types*\n- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;\n- Define your own fitness function for optimization or use a pre-defined function.\n- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.\n\n#### *Machine Learning Weight Optimization*\n- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;\n- Supports classification and regression neural networks.\n\n## Installation\nmlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).\n\nThe latest released version is available at the [Python package index](https://pypi.org/project/mlrose/) and can be installed using `pip`:\n```\npip install mlrose\n```\n\n## Documentation\nThe official mlrose documentation can be found [here](https://mlrose.readthedocs.io/). \n\nA Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).\n\n## Licensing, Authors, Acknowledgements\nmlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). \n\nYou can cite mlrose in research publications and reports as follows:\n* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.\n\nBibTeX entry:\n```\n@misc{Hayes19,\n author = {Hayes, G},\n title \t= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},\n year \t= 2019,\n howpublished = {\\url{https://github.com/gkhayes/mlrose}},\n note \t= {Accessed: day month year}\n}\n```\n\n\n",
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