Metadata-Version: 2.1
Name: sklearn-sfa
Version: 0.1.2
Summary: A scikit-learn compatible implementation of Slow Feature Analysis
Home-page: https://github.com/wiskott-lab/sklearn-sfa
Maintainer: Merlin Schüler
Maintainer-email: merlin.schueler@ini.ruhr-uni-bochum.de
License: new BSD
Download-URL: https://github.com/wiskott-lab/sklearn-sfa
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Provides-Extra: docs
Requires-Dist: sphinx ; extra == 'docs'
Requires-Dist: sphinx-gallery ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
Requires-Dist: matplotlib ; extra == 'docs'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'

.. -*- mode: rst -*-

sklearn-sfa - An implementation of Slow Feature Analysis compatible with scikit-learn
=====================================================================================

.. _scikit-learn: https://scikit-learn.org

.. _documentation: https://sklearn-sfa.readthedocs.io/en/latest/index.html

**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.

Installation 
------------

The package can be installed via *pip*:

.. code-block:: bash

  pip install --user sklearn-sfa


Basic usage
-----------

In Python 3.6+, the package can then be imported as 

.. code-block:: python

  import sksfa 

The package comes with an SFA transformer. Below you see an example of initializing a transformer that
extracts 2-dimensional features:

.. code-block:: python

  sfa_transformer = sksfa.SFA(n_components=2)

The transformer implements sklearn's typical interface by providing ``fit``, ``fit_transform``, and ``transform`` methods.


