Adapted to Python 3.
Original Author - Varun Raj Kompella. (https://varunrajk.gitlab.io)
This is a free software; you can redistribute it and/or modify it. The code is distributed in the hope that it will be useful.
If you plan to use this code in your research please cite: V. R. Kompella, M. Luciw and J. Schmidhuber. "Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams", Neural Computation Journal, Vol. 24 (11), pp. 2994--3024, 2012.
Extract slowly varying components from the input data incrementally. More information about Incremental Slow Feature Analysis can be found in:
Some of the terminology used in the code is inspired from MDP toolkit (www.mdp-toolkit.sourceforge.net)
- ccipca.py : Candid Covariance-Free Incremental PCA module
- mca.py : Minor Component Analysis module
- incsfa.py : Incremental Slow Feature Analysis module
- signalstats.py : Incremental signal stats modules
- trainer.py : Trainer module used for training the modules (modes: 'Incremental', 'BlockIncremental', 'Batch')
- test_incsfa.py : Test example code for IncSFA
- SFA_Node.py : Simple-to-use interface for IncSFA
- MDP toolkit for test_incsfa
- PyQTGraph for fast animated plotting (default is matplotlib)