Metadata-Version: 1.1
Name: metric-learn
Version: 0.5.0
Summary: Python implementations of metric learning algorithms
Home-page: http://github.com/metric-learn/metric-learn
Author: ['CJ Carey', 'Yuan Tang']
Author-email: ccarey@cs.umass.edu
License: MIT
Description: |Travis-CI Build Status| |License| |PyPI version| |Code coverage|
        
        metric-learn
        =============
        
        Metric Learning algorithms in Python.
        
        **Algorithms**
        
        -  Large Margin Nearest Neighbor (LMNN)
        -  Information Theoretic Metric Learning (ITML)
        -  Sparse Determinant Metric Learning (SDML)
        -  Least Squares Metric Learning (LSML)
        -  Neighborhood Components Analysis (NCA)
        -  Local Fisher Discriminant Analysis (LFDA)
        -  Relative Components Analysis (RCA)
        -  Metric Learning for Kernel Regression (MLKR)
        -  Mahalanobis Metric for Clustering (MMC)
        
        **Dependencies**
        
        -  Python 2.7+, 3.4+
        -  numpy, scipy, scikit-learn>=0.20.3
        
        **Optional dependencies**
        
        - For SDML, using skggm will allow the algorithm to solve problematic cases
          (install from commit `a0ed406 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_).
        -  For running the examples only: matplotlib
        
        **Installation/Setup**
        
        Run ``pip install metric-learn`` to download and install from PyPI.
        
        Run ``python setup.py install`` for default installation.
        
        Run ``pytest test`` to run all tests (you will need to have the ``pytest``
        package installed).
        
        **Usage**
        
        See the `sphinx documentation`_ for full documentation about installation, API, usage, and examples.
        
        
        .. _sphinx documentation: http://metric-learn.github.io/metric-learn/
        
        .. |Travis-CI Build Status| image:: https://api.travis-ci.org/metric-learn/metric-learn.svg?branch=master
           :target: https://travis-ci.org/metric-learn/metric-learn
        .. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
           :target: http://badges.mit-license.org
        .. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg
           :target: http://badge.fury.io/py/metric-learn
        .. |Code coverage| image:: https://codecov.io/gh/metric-learn/metric-learn/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/metric-learn/metric-learn
        
Keywords: Metric Learning,Large Margin Nearest Neighbor,Information Theoretic Metric Learning,Sparse Determinant Metric Learning,Least Squares Metric Learning,Neighborhood Components Analysis,Local Fisher Discriminant Analysis,Relative Components Analysis,Mahalanobis Metric for Clustering,Metric Learning for Kernel Regression
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
