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version](http://img.shields.io/pypi/v/pystruct.svg?style=flat)](https://pypi.python.org/pypi/pystruct/)\n[![licence](http://img.shields.io/badge/licence-BSD-blue.svg?style=flat)](https://github.com/pystruct/pystruct/blob/master/LICENSE)\n[![DOI](https://zenodo.org/badge/21369/pystruct/pystruct.svg)](https://zenodo.org/badge/latestdoi/21369/pystruct/pystruct)\n\n\n\nPyStruct\n========\n\nPyStruct aims at being an easy-to-use structured learning and prediction library.\nCurrently it implements only max-margin methods and a perceptron, but other algorithms\nmight follow.\n\nThe goal of PyStruct is to provide a well-documented tool for researchers as well as non-experts\nto make use of structured prediction algorithms.\nThe design tries to stay as close as possible to the interface and conventions\nof [scikit-learn](http://scikit-learn.org).\n\nYou can install pystruct using\n\n\u003e pip install pystruct\n\nSome of the functionality (namely OneSlackSSVM and NSlackSSVM) requires that cvxopt is installed.\nSee the [installation instructions](http://pystruct.github.io/intro.html) for more details.\n\nThe full documentation and installation instructions can be found at the website:\nhttp://pystruct.github.io\n\nYou can contact the authors either via the [mailing list](https://groups.google.com/forum/#!forum/pystruct)\nor on [github](https://github.com/pystruct/pystruct).\n\nCurrently the project is mostly maintained by Andreas Mueller, but contributions are very welcome.\n\nJean-Luc Meunier (Naver Labs Europe) contributed a new model and did some maintenance, in the course of the EU READ project. 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