Python utilities based on theano and pynnet and scikits.learn to learn vector encoders that map vector data to either:

• dense codes in low dimensional space, useful for semantic mapping and visualization by trying to preserve the local structure
• sparse codes in medium to high dimensional space, useful for semantic indexing, denoising and compression

This is experimental code. Nothing is expected to work as advertised yet :)

Implemented:

• deterministic (optimal) sparse encoding using an existing dictionary and Least Angle Regression (see codemaker.sparse)

Work in progress:

• stochastic neighbor embedding in low dim space using autoencoders

Planned:

• stochastic dictionary learning and approximate sparse coding using sparsity inducing autoencoders (see Ranzato 2007)

MIT: http://www.opensource.org/licenses/mit-license.php

Download the source distrib of the afore mentionned dependencies, untar them in the parent folder of codemaker, build scikits.learn in local mode with python setup build_ext -i and setup the dev environment with:

$ . ./activate.sh

You should now be able to fire you favorite python shell and import the codemaker package:

>>> import codemaker
>>> help(codemaker)

Run the tests with the nosetests command.

Sample usage can be found in the examples folder. Lower level usage patterns can also be found in the tests folder.

Failed attempt at using the codemaker embedding utility to extract a 2D manifold from a toy dataset.