.. PyRCN documentation master file, created by
   sphinx-quickstart on Tue Oct 26 11:53:37 2021.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

=====
PyRCN
=====

**A Python 3 framework for building Reservoir Computing Networks (RCNs).**

.. image:: https://badge.fury.io/py/PyRCN.svg
    :target: https://badge.fury.io/py/PyRCN


PyRCN ("Python Reservoir Computing Networks") is a light-weight and transparent Python 3
framework for Reservoir Computing and is based on widely used scientific Python packages,
such as numpy or scipy.

The API is fully `scikit-learn <https://scikit-learn.org/stable>`_-compatible, so that
users of scikit-learn do not need to refactor their code in order to use the estimators
implemented by this framework.
Scikit-learn's built-in parameter optimization methods and example datasets can also be
used in the usual way. PyRCN is used by the `Chair of Speech Technology and Cognitive
Systems, Institute for Acoustics and Speech Communications, Technische Universität
Dresden, Dresden, Germany <https://tu-dresden.de/ing/elektrotechnik/ias/stks?set_language=en>`_
and `IDLab (Internet and Data Lab), Ghent University, Ghent, Belgium <https://www.ugent.be/ea/idlab/en>`_

Currently, it implements Echo State Networks (ESNs) by Herbert Jaeger and Extreme
Learning Machines (ELMs) by Guang-Bin Huang in different flavors,
e.g. Classifier and Regressor.
It is actively developed to be extended into several directions:

* Interaction with `sktime <https://sktime.org/>`_
* Interaction with `hmmlearn <https://hmmlearn.readthedocs.io/en/stable/>`_
* More towards future work: Related architectures, such as Liquid State Machines (LSMs)
and Perturbative Neural Networks (PNNs)

PyRCN has successfully been used for several tasks:

* Music Information Retrieval (MIR)

  * Multipitch tracking

  * Onset detection

  * *f*\ :sub:`0`\  analysis of spoken language

  * GCI detection in raw audio signals

* Time Series Prediction

  * Mackey-Glass benchmark test

  * Stock price prediction

* Ongoing research tasks:

  * Beat tracking in music signals

  * Pattern recognition in sensor data

  * Phoneme recognition

  * Unsupervised pre-training of RCNs and optimization of ESNs

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   installation
   introduction
   getting_started
   tutorial
   development
   citation
   api/api

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

Citation
========

If you use PyRCN, please cite the following publication:

.. code-block:: latex

    @misc{steiner2021pyrcn,
          title={PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks},
          author={Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
          year={2021},
          eprint={2103.04807},
          archivePrefix={arXiv},
          primaryClass={cs.LG}
    }


Acknowledgements
----------------

This research was funded by the European Social Fund (Application number: 100327771) and co-financed by tax funds based on the budget approved by the members of the Saxon State Parliament, and by Ghent University.

.. image:: _static/img/SMWA_EFRE-ESF_Sachsen_Logokombi_quer_03.jpg
  :height: 90
  :alt: Europäischer Sozialfonds

.. image:: _static/img/Logo_IDLab_White.png
  :height: 70
  :alt: IDLab

.. image:: _static/img/logo_UGent_EN_RGB_2400_color-on-white.png
  :height: 70
  :alt: Ghent University

.. image:: _static/img/Logo-STKS.jpg
  :height: 70
  :alt: Kognitive Systeme und Sprachtechnologie

.. image:: _static/img/TUD_Logo_HKS41_114.png
  :height: 70
  :alt: Ghent University