Python implementations of metric learning algorithms

## Project description

## 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)

**Dependencies**

Python 2.6+

numpy, scipy, scikit-learn

(for running the examples only: matplotlib)

**Installation/Setup**

Run python setup.py install for default installation.

Run python setup.py test to run all tests.

**Usage**

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.

**Notes**

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.

**TODO**

implement the rest of the methods on this site

## Project details

## Release history Release notifications | RSS feed

## Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.