Python implementations of metric learning algorithms
Metric Learning algorithms in Python.
- Large Margin Nearest Neighbor (LMNN)
- Information Theoretic Metric Learning (ITML)
- Sparse Determinant Metric Learning (SDML)
- Least Squares Metric Learning (LSML)
- Neighborhood Components Analysis (NCA)
- Python 2.6+
- numpy, scipy, scikit-learn
- (for running the examples only: matplotlib)
Run python setup.py install for default installation.
Run python setup.py test to run all tests.
For full usage examples, see the test and examples directories.
Each metric is a subclass of BaseMetricLearner, which provides default implementations for the methods metric, transformer, and transform. Subclasses must provide an implementation for either metric or transformer.
For an instance of a metric learner named foo learning from a set of d-dimensional points, foo.metric() returns a d by d matrix M such that a distance between vectors x and y is expressed (x-y).dot(M).dot(x-y).
In the same scenario, foo.transformer() returns a d by d matrix L such that a vector x can be represented in the learned space as the vector L.dot(x).
For convenience, the function foo.transform(X) is provided for converting a matrix of points (X) into the learned space, in which standard Euclidean distance can be used.
If a recent version of the Shogun Python modular (modshogun) library is available, the LMNN implementation will use the fast C++ version from there. The two implementations differ slightly, and the C++ version is more complete.
- implement the rest of the methods on this site
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size metric-learn-0.1.0.tar.gz (8.4 kB)||File type Source||Python version None||Upload date||Hashes View|