{ "info": { "author": "Jan Gosmann", "author_email": "jan@hyper-world.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Topic :: Scientific/Engineering", "Topic :: Software Development" ], "description": ".. image:: https://travis-ci.org/jgosmann/goppy.svg?branch=master\n :target: https://travis-ci.org/jgosmann/goppy\n.. image:: https://coveralls.io/repos/jgosmann/goppy/badge.png?branch=master\n :target: https://coveralls.io/r/jgosmann/goppy?branch=master\n\nOverview\n--------\n\nGopPy (Gaussian Online Processes for Python) is a pure Python module providing\na Gaussian process implementation which allows to add new data efficiently\nonline. I wrote this module because all other Python implementations I knew did\nnot support efficient online updates.\n\nThe feature list:\n\n* `scikit-learn `_ compatible interface.\n* Efficient online updates.\n* Prediction of first order derivatives.\n* Estimation of the log likelihood and its derivative.\n* Well documented.\n* `Good test coverage. `_\n* Supports Python 2.6, 2.7, 3.3, 3.4, and 3.5. 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