https://github.com/lcav/sketchrls

Sketch RLS is an adaptive filtering algorithm that brings sketching ideas into the classical recursive least squares algorithm. This is the python implementation of the algorithm.
https://github.com/lcav/sketchrls

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Sketch RLS is an adaptive filtering algorithm that brings sketching ideas into the classical recursive least squares algorithm. This is the python implementation of the algorithm.

• Host: GitHub
• URL: https://github.com/lcav/sketchrls
• Owner: LCAV
• Created: 2016-01-21T10:24:47.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2016-01-21T10:36:34.000Z (almost 9 years ago)
• Last Synced: 2024-05-14T00:13:01.676Z (8 months ago)
• Language: Python
• Size: 4.83 MB
• Stars: 9
• Watchers: 5
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
The Recursive Hessian Sketch for Adaptive Filtering

===================================================

This is the companion code that was used to produce the figures

of the paper __The Recursive Hessian Sketch for Adaptive Filtering__

by Robin Scheibler and Martin Vetterli, submitted to ICASSP 2016.

Authors

-------

Robin Scheibler, and Martin Vetterli are with 

Laboratory for Audiovisual Communications ([LCAV](http://lcav.epfl.ch)) at 

[EPFL](http://www.epfl.ch).



#### Contact

[Robin Scheibler](mailto:ivan[dot]dokmanic[at]epfl[dot]ch) 


EPFL-IC-LCAV 


BC Building 


Station 14 


1015 Lausanne

Run the code

------------

All the code is pure python and uses only numpy, scipy, matplotlib. The code was

run with ipython.

    $ ipython --version

    3.2.1

We use anaconda to install python, numpy, matplotlib, etc.

### Code organization

All the classical adaptive filters are implemented in `adaptive_filters.py`.

The proposed algorithm is in `sketch_rls.py`.

### Figures 2. 

Simply run

    $ ipython ./figure_Complexity.py

### Figures 3.

Start an ipython cluster in the repository.

    $ ipcluster start -n x

where `x` is the number of engines you want to use. You can change the number

of loops directly in the script line 42. Then, run the command

    $ ipython figure_MSE_sim.py

This will run the long simulation needed. The result will be stored

in the folder `sim_data` and the name of the file will contain the date and time.

Copy the date and time in the file `figure_MSE_plot.py` line 61-64. Then run

    $ ipython figure_MSE_plot.py

Finally, the file `figure_MSE_test.py` allows to be quickly edited to test

different parameters.

    $ ipython figure_MSE_test.py

License

-------

Copyright (c) 2016, LCAV

This code is free to reuse for non-commercial purpose such as academic or

educational. For any other use, please contact the authors.








Sketch RLS 

by LCAV, EPFL 

is licensed under a 

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


Based on a work at https://github.com/LCAV/sketchrls.