PyRCN

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

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-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 and IDLab (Internet and Data Lab), Ghent University, Ghent, Belgium

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
• Interaction with hmmlearn
• 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
  • f₀ 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

Contents:

• Installation guide
  • Installation using pip
  • Installation from source
• Definition of Reservoir Computing
• Getting started
  • Playing with blocks – Building blocks of Reservoir Computing
• Tutorials
• Development
  • How can you contribute:
• Citation
• PyRCN API
  • PyRCN package
  • pyrcn.base
  • pyrcn.echo_state_network
  • pyrcn.extreme_learning_machine
  • pyrcn.linear_model

Indices and tables

• Index
• Module Index
• Search Page

Citation

If you use PyRCN, please cite the following publication:

@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.