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        "description": "<p align=\"center\"><img src=\"./assets/images/logo.png\" /></p>\n\n--------------------------------------------------------------------------------\n\n[![Build Status](https://travis-ci.org/seba-1511/randopt.svg?branch=master)](https://travis-ci.org/seba-1511/randopt)\n[![PyPI version](https://badge.fury.io/py/randopt.svg)](https://badge.fury.io/py/randopt)\n\nrandopt is a Python package for machine learning experiment management, hyper-parameter optimization, and results visualization. Some of its features include:\n\n* result logging and management,\n* human-readable format,\n* support for parallelism / distributed / asynchronous experiments,\n* command-line and programmatic API,\n* shareable, flexible Web visualization,\n* automatic hyper-parameter search, and\n* pure Python - no dependencies !\n\n\n# Installation\n\n```shell\npip install randopt\n```\n\n# Usage\n\n```python\nimport randopt as ro\n\ndef loss(x):\n    return x**2\n\ne = ro.Experiment('myexp', {\n        'alpha': ro.Gaussian(mean=0.0, std=1.0, dtype='float'),\n    })\n\n# Sampling parameters\nfor i in xrange(100):\n    e.sample('alpha')\n    res = loss(e.alpha)\n    print('Result: ', res)\n    e.add_result(res)\n\n# Manually setting parameters\ne.alpha = 0.00001\nres = loss(e.alpha)\ne.add_result(res)\n\n# Search over all experiments results, including ones from previous runs\nopt = e.minimum()\nprint('Best result: ', opt.result, ' with params: ', opt.params)\n```\n\n## Results Visualization\n\nOnce you obtained some results, run `roviz.py path/to/experiment/folder` to visualize them in your web browser.\n\nFor more info on visualization and `roviz.py`, refer to the [Visualizing Results](https://github.com/seba-1511/randopt/wiki/Visualizing-Results) tutorial.\n\n## Hyper-Parameter Optimization\n\nTo generate results and search for good hyper-parameters you can either user `ropt.py` or write your own optimizaiton script using the [Evolutionary](https://github.com/seba-1511/randopt/wiki/evolutionary) and [GridSearch](https://github.com/seba-1511/randopt/wiki/grid_search) classes.\n\nFor more info on hyper-parameter optimization, refer to the [Optimizing Hyperparams](https://github.com/seba-1511/randopt/wiki/Optimizing-Hyperparams) tutorial.\n\n\n# Documentation\n\nFor more examples, tutorials, and documentation refer to the [wiki](https://github.com/seba-1511/randopt/wiki).\n\n\n# Contributing\n\nTo contribute to Randopt, it is recommended to follow the [contribution guidelines](CONTRIBUTING.md).\n\n### Acknowledgements\nRandopt is maintained by [S\u00e9b Arnold](http://seba1511.com), with numerous contributions from the following persons.\n\n* Noel Trivedi\n* Cyrus Jia\n* Daler Asrorov\n\n# License\n\nRandopt is released under the Apache 2 License. For more information, refer to the [LICENSE file](LICENSE.txt).\n\nI would love to hear how your use Randopt. Feel free to [drop me a line](http://seba1511.com) !",
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