2020-04-18: NEW: support libsvm for Python 3 libsvm has been updated to be
compatible with Python >= 3.4. Python 2 support has been dropped,
so we don't test it anymore.
2020-04-18: ERF: restore Python 2 compatibility and add a matrix entry for it
in travis
2019-18-12: FIX: SciPy 1.4 support.
2019-13-12: NEW: VartimeSFANode supports SFA on data with non-constant time
increments. This yields an SFA implementation that is robust
against missing data.
2019-01-20: NEW: Added a new family of expansion nodes, including Legendre,
Hermite and Chebyshev polynomials. A facility for easily including
recursively defined 1-dimensional expansion functions powers these
nodes, applying them to N-dimensional data by tensor basis
construction. The new expansion nodes are numerically more stable
than ordinary monomials and permit higher expansion degrees.
Examples where numerically stable expansion is beneficial:
https://github.com/NiMlr/pyfunctionbases
https://arxiv.org/abs/1805.08565, section 4.2.1, Figure 11 and A.2.
2019-01-19: FIX: pytest Python 3.7 compatibility
2018-07-18: FIX: PEP 479 compatibility
2018-06-15: NEW: Added SFA-based supervised learning, specifically graph based
SFA nodes GSFANode and iGSFANode and hierarchical GSFA (HGSFA).
For further information, see:
Escalante-B A.-N., Wiskott L, "How to solve classification and
regression problems on high-dimensional data with a supervised
extension of Slow Feature Analysis". Journal of Machine Learning
Research 14:3683-3719, 2013.
http://www.jmlr.org/papers/volume14/escalante13a/escalante13a.pdf
https://github.com/AlbertoEsc/cuicuilco
2018-05-29: DOC: Revamped the documentation for Sphinx-autodoc compatibility.
MDP documentation is now available on readthedocs:
https://mdpdocs.readthedocs.io/en/latest
2018-01-18: NEW: Added solvers to SFA-node that are robust against rank deficit
in the covariance matrix. When data contained linear redundancies,
like easily introduced by e.g. insufficient cropping of black bars
of a video, this usually resulted in
SymeigException ('Covariance matrices may be singular').
Now it provides simple instructions for robust processing of such
data by setting a new flag in SFANode:
rank_deficit_method can be 'none' (default), 'reg', 'pca', 'svd',
leveraging robust solving via regularization, PCA, SVD or LDL
decomposition respectively. (LDL requires SciPy >= 1.0)
2017-03-11: DOC: changed reference to new mailing list
old: mdp-toolkit-userslists.sourceforge.net
new: mdp-toolkitpython.org
2017-03-06: NEW: Added online mode to enable use of MDP in reinforcement
learning. This notably includes incremental SFA as described in
"Varun Raj Kompella, Matthew Luciw, and Juergen Schmidhuber (2012).
Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow
Feature Updating from High-Dimensional Input Streams.
Neural Computation."
https://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00344
New nodes and flows:
OnlineNode, OnlineFlow, CircularFlow, OnlineCenteringNode,
OnlineTimeDiffNode, OnlineFlowNode, CircularOnlineFlowNode,
OnlineLayer, CloneOnlineLayer, SameInputOnlineLayer, MCANode,
CCIPCANode, IncSFANode
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