A scikit-learn compatible implementation of Slow Feature Analysis
Project description
sklearn-sfa or sksfa is an implementation of Slow Feature Analysis for scikit-learn.
It is meant as a standalone transformer for dimensionality reduction or as a building block for more complex representation learning pipelines utilizing scikit-learn’s extensive collection of machine learning methods.
The package contains a solver for linear SFA and some auxiliary functions. The documentation provides an explanation of the algorithm, different use-cases, as well as pointers how to fully utilize SFA’s potential, e.g., by employing non-linear basis functions or more sophisticated architectures.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for sklearn_sfa-0.1.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 76edd37d13aaee9bbfc30802c17c91c3d27627e0f886720e22a74a7fde2d91f8 |
|
MD5 | 528e61cd9f663169acbd9a36a8dda40a |
|
BLAKE2b-256 | 2bb96b7637b5ac780d3332ba8d748dbe70b18104cfb4356c2afd03568a07b96d |