Metadata-Version: 2.1
Name: PyRCN
Version: 0.0.3
Summary: A Python3 framework for Reservoir Computing with a scikit-learn-compatible API
Home-page: https://github.com/TUD-STKS/PyRCN
Author: Peter Steiner
Author-email: peter.steiner@tu-dresden.de
License: UNKNOWN
Project-URL: Documentation, https://github.com/TUD-STKS/PyRCN
Project-URL: Funding, https://github.com/TUD-STKS/PyRCN
Project-URL: Source, https://github.com/TUD-STKS/PyRCN
Project-URL: Tracker, https://github.com/TUD-STKS/PyRCN/issues
Description: # PyRCN
        A Python 3 framework for Reservoir Computing with a scikit-learn-compatible API.
        
        PyRCN is a light-weight and transparent Python 3 framework that implements ESNs 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 restructure their research data in order to use ESNs. Interested used can directly use scikit-learns built-in parameter optimization methods and example datasets.
        
        # Getting Started
        
        PyRCN includes currently two variantes of Echo State Networks (ESNs): The ESNClassifier is meant to be a classifier, the ESNRegressor is meant to be a regressor.
        
        Basic example for the ESNClassifier:
        
        ```python
        from pyrcn.echo_state_network import ESNClassifier
        
        
        clf = ESNClassifier()
        clf.fit(X=X_train, y=y_train)
        
        y_pred_classes = clf.predict(X=X_test)  # output is the class for each input example
        y_pred_proba = clf.predict_proba(X=X_test)  #  output are the class probabilities for each input example
        ```
        
        Basic example for the ESNRegressor:
        
        ```python
        from pyrcn.echo_state_network import ESNRegressor
        
        
        reg = ESNRegressor()
        ref.fit(X=X_train, y=y_train)
        
        y_pred_classes = reg.predict(X=X_test)  # output is the prediction for each input example
        ```
        
        # Acknowledgements
        This research is financed by EuropÃ¤ischer Sozialfonds (ESF), the Free State of Saxony and Ghent University.
        
        ![SMWA_EFRE-ESF Logo](https://github.com/TUD-STKS/PyRCN/blob/master/images/SMWA_EFRE-ESF_Sachsen_Logokombi_quer_03.jpg&s=200)
        ![Ghent University](https://github.com/TUD-STKS/PyRCN/blob/master/images/logo_UGent_EN_RGB_2400_color-on-white.png) ![IDLab](https://github.com/TUD-STKS/PyRCN/blob/master/images/Logo_IDLab_White.png&s=200)
        
        # References
        If you use the PyRCN, please cite the following publication:
        
        ```latex
        @INPROCEEDINGS{src:PyRCN-20,  
        	author={Peter Steiner and Simon Stone and Azarakhsh Jalalvand and Peter Birkholz},  
        	booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},   
        	title={Feature Engineering and Stacked ESNs for Musical Onset Detection},  
        	year={2020},  
        	volume={},  
        	number={},  
        	note={submitted},
        }
        ```
        
Keywords: PyRCN,Echo State Network
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
