https://github.com/scikit-learn-contrib/metric-learn
Tip revision: 57086e91b65b88a95c89449aa501ff68a61dc39a authored by William de Vazelhes on 18 July 2019, 14:24:46 UTC
Prepare new version (#232)
Prepare new version (#232)
Tip revision: 57086e9
README.rst
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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/
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