This Python package contains some variants of the matching pursuit sparse coding algorithm. Matching pursuit uses a set of "basis functions" or "codebook filters" to greedily encode a raw signal in terms of a weighted sum of filters. Using gradient ascent on the likelihood of an observed dataset, we can also infer a likely set of filters from an unlabeled dataset of signals.
Just use the setup.py script :
python setup.py install
Or use pip and virtualenv for even more installation goodness :
pip install lmj.pursuit
After installation, you can use the package by importing lmj.pursuit.
The source distribution includes three tests: gaussian, sound and image. The gaussian test demonstrates using an overcomplete dictionary to encode points drawn from a mixture of gaussians. The image test encodes image pixels. The sound test uses an experimental implementation that runs on a CUDA-enabled graphics device to encode sound waveforms.
You'll need cairo and GTK on your machine to run this test :
pip install pygtk pycairo
Then just run the test :
A window will pop up that shows a small number of gaussian centroids arranged around a central representation of a codebook of 2D basis vectors. Press the space bar to start training, and points will be sampled from the centroids and used to train the matching pursuit codebook. Eventually, vectors in the codebook should point towards the centroids.
You'll need to install glumpy to run this test :
pip install glumpy
The image test simply requires some image data to run :
python test/images.py /path/to/my/image*.jpg /path/to/another/image*.png
You'll see a window appear on your desktop ; this window is divided into four quadrants. At the upper-left is an image to be encoded. On the upper-right is the reconstructed image. In the lower-left are the codebook filters being used to perform the encoding. In the lower-right are the "feature maps" that show where each codebook filter has been used to reconstruct the source image.
The sound test runs matching pursuit on your 64-bit graphics card. To get started, install py-cuda :
pip install pycuda
Currently, this test requires that your graphics device support 64-bit floating
point values. If your graphics device is limited to 32-bit floats, you can add
bit_depth=32 to the CudaPursuit constructor in the test.
The pursuit algorithm is trained using a sound waveform and reports the error after encoding and decoding a test sound -- smaller numbers are better. Run this test with :
The general takeaway is that signal reproduction tends to improve with more training (successive numbers within a group), with more codebook filters (first column), and by using the multiple-frame (convolution) encoder instead of the single-frame (standard) encoder. Interestingly, the standard encoder tends to do worse with larger filters (second column), while the convolution encoder tends to do worse with smaller filters.
If you have matplotlib installed, you can also save plots of the codebook
vectors during training by setting
GRAPHS = '/tmp/pursuit' (or some other
directory name) in
test/sound.py. Graphing doubles the test runtime, but
produces some pretty training artifacts.
(The MIT License)
Copyright (c) 2010 Leif Johnson email@example.com
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