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        "description": "|Travis-CI Build Status| |License| |PyPI version| |Code coverage|\n\nmetric-learn\n=============\n\nMetric Learning algorithms in Python.\n\n**Algorithms**\n\n-  Large Margin Nearest Neighbor (LMNN)\n-  Information Theoretic Metric Learning (ITML)\n-  Sparse Determinant Metric Learning (SDML)\n-  Least Squares Metric Learning (LSML)\n-  Neighborhood Components Analysis (NCA)\n-  Local Fisher Discriminant Analysis (LFDA)\n-  Relative Components Analysis (RCA)\n-  Metric Learning for Kernel Regression (MLKR)\n-  Mahalanobis Metric for Clustering (MMC)\n\n**Dependencies**\n\n-  Python 2.7+, 3.4+\n-  numpy, scipy, scikit-learn>=0.20.3\n\n**Optional dependencies**\n\n- For SDML, using skggm will allow the algorithm to solve problematic cases\n  (install from commit `a0ed406 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_).\n-  For running the examples only: matplotlib\n\n**Installation/Setup**\n\nRun ``pip install metric-learn`` to download and install from PyPI.\n\nRun ``python setup.py install`` for default installation.\n\nRun ``pytest test`` to run all tests (you will need to have the ``pytest``\npackage installed).\n\n**Usage**\n\nSee the `sphinx documentation`_ for full documentation about installation, API, usage, and examples.\n\n\n.. _sphinx documentation: http://metric-learn.github.io/metric-learn/\n\n.. |Travis-CI Build Status| image:: https://api.travis-ci.org/metric-learn/metric-learn.svg?branch=master\n   :target: https://travis-ci.org/metric-learn/metric-learn\n.. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat\n   :target: http://badges.mit-license.org\n.. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg\n   :target: http://badge.fury.io/py/metric-learn\n.. |Code coverage| image:: https://codecov.io/gh/metric-learn/metric-learn/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/metric-learn/metric-learn\n\n\n",
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