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| author | CoprDistGit <infra@openeuler.org> | 2023-05-31 03:12:58 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-31 03:12:58 +0000 |
| commit | 918cb0ea62aaa1ba75a569c66bb56c1b3f2b2891 (patch) | |
| tree | e14966bec7ced6c90bac4de9f388efa574d65525 | |
| parent | d44eabe8939802f31ea597ccc0ee260793649e8f (diff) | |
automatic import of python-pyrcn
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-pyrcn.spec | 996 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 998 insertions, 0 deletions
@@ -0,0 +1 @@ +/PyRCN-0.0.17.post1.tar.gz diff --git a/python-pyrcn.spec b/python-pyrcn.spec new file mode 100644 index 0000000..145493f --- /dev/null +++ b/python-pyrcn.spec @@ -0,0 +1,996 @@ +%global _empty_manifest_terminate_build 0 +Name: python-PyRCN +Version: 0.0.17.post1 +Release: 1 +Summary: A scikit-learn-compatible framework for Reservoir Computing in Python +License: BSD License +URL: https://github.com/TUD-STKS/PyRCN +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d0/04/9058f0086e1d60250723781ef5a5bfc39177887e66f56aac2201471a9659/PyRCN-0.0.17.post1.tar.gz +BuildArch: noarch + + +%description +# PyRCN
+**A Python 3 framework for Reservoir Computing with a [scikit-learn](https://scikit-learn.org/stable/)-compatible API.**
+
+[](https://badge.fury.io/py/PyRCN)
+[](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+
+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](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](http://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
+ - $f_{0}$ 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
+
+Please see the [References section](#references) for more information. For code examples, see [Getting started](#getting-started).
+
+## Installation
+
+### Prerequisites
+
+PyRCN is developed using Python 3.7 or newer. It depends on the following packages:
+
+- [numpy>=1.18.1](https://numpy.org/)
+- [scikit-learn>=1.0](https://scikit-learn.org/stable/)
+- [joblib>=0.13.2](https://joblib.readthedocs.io)
+- [pandas>=1.0.0](https://pandas.pydata.org/)
+- [matplotlib](https://matplotlib.org/)
+- [seaborn](https://seaborn.pydata.org/)
+
+### Installation from PyPI
+
+The easiest and recommended way to install ``PyRCN`` is to use ``pip`` from [PyPI](https://pypi.org) :
+
+```
+pip install pyrcn
+```
+
+### Installation from source
+
+If you plan to contribute to ``PyRCN``, you can also install the package from source.
+
+Clone the Git repository:
+
+```
+git clone https://github.com/TUD-STKS/PyRCN.git
+```
+
+Install the package using ``setup.py``:
+```
+python setup.py install --user
+```
+
+## Offcial documentation
+
+See [the official PyRCN documentation](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+to learn more about the main features of PyRCN, its API and the installation process.
+
+## Package structure
+The package is structured in the following way:
+
+- `doc`
+ - This folder includes the package documentation.
+- `examples`
+ - This folder includes example code as Jupyter Notebooks and python scripts.
+- `images`
+ - This folder includes the logos used in ´README.md´.
+- `pyrcn`
+ - This folder includes the actual Python package.
+
+
+## Getting Started
+
+PyRCN includes currently variants of Echo State Networks (ESNs) and Extreme Learning Machines (ELMs): Regressors and Classifiers.
+
+Basic example for the ESNClassifier:
+
+```python
+from sklearn.model_selection import train_test_split
+from pyrcn.datasets import load_digits
+from pyrcn.echo_state_network import ESNClassifier
+
+X, y = load_digits(return_X_y=True, as_sequence=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+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.datasets import mackey_glass
+from pyrcn.echo_state_network import ESNRegressor
+
+X, y = mackey_glass(n_timesteps=20000)
+
+reg = ESNRegressor()
+reg.fit(X=X[:8000], y=y[:8000])
+
+y_pred = reg.predict(X[8000:]) # output is the prediction for each input example
+```
+
+An extensive introduction to getting started with PyRCN is included in the [examples](https://github.com/TUD-STKS/PyRCN/blob/main/examples) directory.
+The notebook [digits](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.py) show how to set up an ESN for a small hand-written digit recognition experiment.
+Launch the digits notebook on Binder:
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fdigits.ipynb)
+
+The notebook [PyRCN_Intro](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.py) show how to construct different RCNs with building blocks.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2FPyRCN_Intro.ipynb)
+
+The notebook [Impulse responses](https://github.com/TUD-STKS/PyRCN/blob/main/examples/esn_impulse_responses.ipynb) is an interactive tool to demonstrate the impact of different hyper-parameters on the impulse responses of an ESN.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fesn_impulse_responses.ipynb)
+
+Fore more advanced examples, please have a look at our [Automatic Music Transcription Repository](https://github.com/TUD-STKS/Automatic-Music-Transcription), in which we provide an entire feature extraction, training and test pipeline for multipitch tracking and for note onset detection using PyRCN. This is currently transferred to this repository.
+
+## Citation
+
+If you use PyRCN, please cite the following publication:
+
+```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}
+}
+```
+
+## References
+
+[Unsupervised Pretraining of Echo State Networks for Onset Detection](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21e,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Igor Farka{\v{s}} and Paolo Masulli and Sebastian Otte and Stefan Wermter",
+ title="{U}nsupervised {P}retraining of {E}cho {S}tate {N}etworks for {O}nset {D}etection",
+ booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2021",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="59--70",
+ isbn="978-3-030-86383-8"
+}
+
+
+```
+
+[Improved Acoustic Modeling for Automatic Piano Music Transcription Using Echo State Networks](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21d,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Ignacio Rojas and Gonzalo Joya and Andreu Catala",
+ title="{I}mproved {A}coustic {M}odeling for {A}utomatic {P}iano {M}usic {T}ranscription {U}sing {E}cho {S}tate {N}etworks",
+ booktitle="Advances in Computational Intelligence",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="143--154",
+ isbn="978-3-030-85099-9"
+}
+```
+
+Glottal Closure Instant Detection using Echo State Networks
+- [Paper](http://www.essv.de/pdf/2021_161_168.pdf)
+- [Repository](https://github.com/TUD-STKS/gci_estimation)
+- Reference
+```latex
+@InProceedings{src:Steiner-21c,
+ title = {Glottal Closure Instant Detection using Echo State Networks},
+ author = {Peter Steiner and Ian S. Howard and Peter Birkholz},
+ year = {2021},
+ pages = {161--168},
+ keywords = {Oral},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2021},
+ editor = {Stefan Hillmann and Benjamin Weiss and Thilo Michael and Sebastian Möller},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-95908-227-3}
+}
+```
+
+Cluster-based Input Weight Initialization for Echo State Networks
+
+```latex
+@article{Steiner2022cluster,
+ author = {Steiner, Peter and Jalalvand, Azarakhsh and Birkholz, Peter},
+ doi = {10.1109/TNNLS.2022.3145565},
+ issn = {2162-2388},
+ journal = {IEEE Transactions on Neural Networks and Learning Systems},
+ keywords = {},
+ month = {},
+ number = {},
+ pages = {1--12},
+ title = {Cluster-based Input Weight Initialization for Echo State Networks},
+ volume = {},
+ year = {2022},
+}
+```
+
+PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks
+
+```latex
+@article{Steiner2022pyrcn,
+ title = {PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks},
+ journal = {Engineering Applications of Artificial Intelligence},
+ volume = {113},
+ pages = {104964},
+ year = {2022},
+ issn = {0952-1976},
+ doi = {10.1016/j.engappai.2022.104964},
+ url = {https://www.sciencedirect.com/science/article/pii/S0952197622001713},
+ author = {Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+}
+```
+
+[Feature Engineering and Stacked ESNs for Musical Onset Detection](https://ieeexplore.ieee.org/abstract/document/9413205)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21b,
+ author={Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+ booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+ title={{F}eature {E}ngineering and {S}tacked {E}cho {S}tate {N}etworks for {M}usical {O}nset {D}etection},
+ year={2021},
+ volume={},
+ number={},
+ pages={9537--9544},
+ doi={10.1109/ICPR48806.2021.9413205}
+}
+```
+
+[Multipitch tracking in music signals using Echo State Networks](https://ieeexplore.ieee.org/abstract/document/9287638)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21a,
+ author={Peter Steiner and Simon Stone and Peter Birkholz and Azarakhsh Jalalvand},
+ booktitle={2020 28th European Signal Processing Conference (EUSIPCO)},
+ title={{M}ultipitch tracking in music signals using {E}cho {S}tate {N}etworks},
+ year={2021},
+ volume={},
+ number={},
+ pages={126--130},
+ keywords={},
+ doi={10.23919/Eusipco47968.2020.9287638},
+ ISSN={2076-1465},
+ month={Jan},
+```
+
+[Note Onset Detection using Echo State Networks](http://www.essv.de/pdf/2020_157_164.pdf)
+
+```latex
+@InProceedings{src:Steiner-20,
+ title = {Note Onset Detection using Echo State Networks},
+ author = {Peter Steiner and Simon Stone and Peter Birkholz},
+ year = {2020},
+ pages = {157--164},
+ keywords = {Poster},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
+ editor = {Ronald Böck and Ingo Siegert and Andreas Wendemuth},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-959081-93-1}
+}
+```
+
+[Multiple-F0 Estimation using Echo State Networks](https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf)
+
+```latex
+@inproceedings{src:Steiner-19,
+ title={{M}ultiple-{F}0 {E}stimation using {E}cho {S}tate {N}etworks},
+ booktitle={{MIREX}},
+ author={Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz},
+ year={2019},
+ url = {https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf}
+}
+```
+
+
+## Acknowledgments
+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.
+ + +%package -n python3-PyRCN +Summary: A scikit-learn-compatible framework for Reservoir Computing in Python +Provides: python-PyRCN +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-PyRCN +# PyRCN
+**A Python 3 framework for Reservoir Computing with a [scikit-learn](https://scikit-learn.org/stable/)-compatible API.**
+
+[](https://badge.fury.io/py/PyRCN)
+[](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+
+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](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](http://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
+ - $f_{0}$ 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
+
+Please see the [References section](#references) for more information. For code examples, see [Getting started](#getting-started).
+
+## Installation
+
+### Prerequisites
+
+PyRCN is developed using Python 3.7 or newer. It depends on the following packages:
+
+- [numpy>=1.18.1](https://numpy.org/)
+- [scikit-learn>=1.0](https://scikit-learn.org/stable/)
+- [joblib>=0.13.2](https://joblib.readthedocs.io)
+- [pandas>=1.0.0](https://pandas.pydata.org/)
+- [matplotlib](https://matplotlib.org/)
+- [seaborn](https://seaborn.pydata.org/)
+
+### Installation from PyPI
+
+The easiest and recommended way to install ``PyRCN`` is to use ``pip`` from [PyPI](https://pypi.org) :
+
+```
+pip install pyrcn
+```
+
+### Installation from source
+
+If you plan to contribute to ``PyRCN``, you can also install the package from source.
+
+Clone the Git repository:
+
+```
+git clone https://github.com/TUD-STKS/PyRCN.git
+```
+
+Install the package using ``setup.py``:
+```
+python setup.py install --user
+```
+
+## Offcial documentation
+
+See [the official PyRCN documentation](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+to learn more about the main features of PyRCN, its API and the installation process.
+
+## Package structure
+The package is structured in the following way:
+
+- `doc`
+ - This folder includes the package documentation.
+- `examples`
+ - This folder includes example code as Jupyter Notebooks and python scripts.
+- `images`
+ - This folder includes the logos used in ´README.md´.
+- `pyrcn`
+ - This folder includes the actual Python package.
+
+
+## Getting Started
+
+PyRCN includes currently variants of Echo State Networks (ESNs) and Extreme Learning Machines (ELMs): Regressors and Classifiers.
+
+Basic example for the ESNClassifier:
+
+```python
+from sklearn.model_selection import train_test_split
+from pyrcn.datasets import load_digits
+from pyrcn.echo_state_network import ESNClassifier
+
+X, y = load_digits(return_X_y=True, as_sequence=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+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.datasets import mackey_glass
+from pyrcn.echo_state_network import ESNRegressor
+
+X, y = mackey_glass(n_timesteps=20000)
+
+reg = ESNRegressor()
+reg.fit(X=X[:8000], y=y[:8000])
+
+y_pred = reg.predict(X[8000:]) # output is the prediction for each input example
+```
+
+An extensive introduction to getting started with PyRCN is included in the [examples](https://github.com/TUD-STKS/PyRCN/blob/main/examples) directory.
+The notebook [digits](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.py) show how to set up an ESN for a small hand-written digit recognition experiment.
+Launch the digits notebook on Binder:
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fdigits.ipynb)
+
+The notebook [PyRCN_Intro](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.py) show how to construct different RCNs with building blocks.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2FPyRCN_Intro.ipynb)
+
+The notebook [Impulse responses](https://github.com/TUD-STKS/PyRCN/blob/main/examples/esn_impulse_responses.ipynb) is an interactive tool to demonstrate the impact of different hyper-parameters on the impulse responses of an ESN.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fesn_impulse_responses.ipynb)
+
+Fore more advanced examples, please have a look at our [Automatic Music Transcription Repository](https://github.com/TUD-STKS/Automatic-Music-Transcription), in which we provide an entire feature extraction, training and test pipeline for multipitch tracking and for note onset detection using PyRCN. This is currently transferred to this repository.
+
+## Citation
+
+If you use PyRCN, please cite the following publication:
+
+```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}
+}
+```
+
+## References
+
+[Unsupervised Pretraining of Echo State Networks for Onset Detection](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21e,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Igor Farka{\v{s}} and Paolo Masulli and Sebastian Otte and Stefan Wermter",
+ title="{U}nsupervised {P}retraining of {E}cho {S}tate {N}etworks for {O}nset {D}etection",
+ booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2021",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="59--70",
+ isbn="978-3-030-86383-8"
+}
+
+
+```
+
+[Improved Acoustic Modeling for Automatic Piano Music Transcription Using Echo State Networks](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21d,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Ignacio Rojas and Gonzalo Joya and Andreu Catala",
+ title="{I}mproved {A}coustic {M}odeling for {A}utomatic {P}iano {M}usic {T}ranscription {U}sing {E}cho {S}tate {N}etworks",
+ booktitle="Advances in Computational Intelligence",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="143--154",
+ isbn="978-3-030-85099-9"
+}
+```
+
+Glottal Closure Instant Detection using Echo State Networks
+- [Paper](http://www.essv.de/pdf/2021_161_168.pdf)
+- [Repository](https://github.com/TUD-STKS/gci_estimation)
+- Reference
+```latex
+@InProceedings{src:Steiner-21c,
+ title = {Glottal Closure Instant Detection using Echo State Networks},
+ author = {Peter Steiner and Ian S. Howard and Peter Birkholz},
+ year = {2021},
+ pages = {161--168},
+ keywords = {Oral},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2021},
+ editor = {Stefan Hillmann and Benjamin Weiss and Thilo Michael and Sebastian Möller},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-95908-227-3}
+}
+```
+
+Cluster-based Input Weight Initialization for Echo State Networks
+
+```latex
+@article{Steiner2022cluster,
+ author = {Steiner, Peter and Jalalvand, Azarakhsh and Birkholz, Peter},
+ doi = {10.1109/TNNLS.2022.3145565},
+ issn = {2162-2388},
+ journal = {IEEE Transactions on Neural Networks and Learning Systems},
+ keywords = {},
+ month = {},
+ number = {},
+ pages = {1--12},
+ title = {Cluster-based Input Weight Initialization for Echo State Networks},
+ volume = {},
+ year = {2022},
+}
+```
+
+PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks
+
+```latex
+@article{Steiner2022pyrcn,
+ title = {PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks},
+ journal = {Engineering Applications of Artificial Intelligence},
+ volume = {113},
+ pages = {104964},
+ year = {2022},
+ issn = {0952-1976},
+ doi = {10.1016/j.engappai.2022.104964},
+ url = {https://www.sciencedirect.com/science/article/pii/S0952197622001713},
+ author = {Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+}
+```
+
+[Feature Engineering and Stacked ESNs for Musical Onset Detection](https://ieeexplore.ieee.org/abstract/document/9413205)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21b,
+ author={Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+ booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+ title={{F}eature {E}ngineering and {S}tacked {E}cho {S}tate {N}etworks for {M}usical {O}nset {D}etection},
+ year={2021},
+ volume={},
+ number={},
+ pages={9537--9544},
+ doi={10.1109/ICPR48806.2021.9413205}
+}
+```
+
+[Multipitch tracking in music signals using Echo State Networks](https://ieeexplore.ieee.org/abstract/document/9287638)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21a,
+ author={Peter Steiner and Simon Stone and Peter Birkholz and Azarakhsh Jalalvand},
+ booktitle={2020 28th European Signal Processing Conference (EUSIPCO)},
+ title={{M}ultipitch tracking in music signals using {E}cho {S}tate {N}etworks},
+ year={2021},
+ volume={},
+ number={},
+ pages={126--130},
+ keywords={},
+ doi={10.23919/Eusipco47968.2020.9287638},
+ ISSN={2076-1465},
+ month={Jan},
+```
+
+[Note Onset Detection using Echo State Networks](http://www.essv.de/pdf/2020_157_164.pdf)
+
+```latex
+@InProceedings{src:Steiner-20,
+ title = {Note Onset Detection using Echo State Networks},
+ author = {Peter Steiner and Simon Stone and Peter Birkholz},
+ year = {2020},
+ pages = {157--164},
+ keywords = {Poster},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
+ editor = {Ronald Böck and Ingo Siegert and Andreas Wendemuth},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-959081-93-1}
+}
+```
+
+[Multiple-F0 Estimation using Echo State Networks](https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf)
+
+```latex
+@inproceedings{src:Steiner-19,
+ title={{M}ultiple-{F}0 {E}stimation using {E}cho {S}tate {N}etworks},
+ booktitle={{MIREX}},
+ author={Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz},
+ year={2019},
+ url = {https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf}
+}
+```
+
+
+## Acknowledgments
+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.
+ + +%package help +Summary: Development documents and examples for PyRCN +Provides: python3-PyRCN-doc +%description help +# PyRCN
+**A Python 3 framework for Reservoir Computing with a [scikit-learn](https://scikit-learn.org/stable/)-compatible API.**
+
+[](https://badge.fury.io/py/PyRCN)
+[](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+
+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](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](http://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
+ - $f_{0}$ 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
+
+Please see the [References section](#references) for more information. For code examples, see [Getting started](#getting-started).
+
+## Installation
+
+### Prerequisites
+
+PyRCN is developed using Python 3.7 or newer. It depends on the following packages:
+
+- [numpy>=1.18.1](https://numpy.org/)
+- [scikit-learn>=1.0](https://scikit-learn.org/stable/)
+- [joblib>=0.13.2](https://joblib.readthedocs.io)
+- [pandas>=1.0.0](https://pandas.pydata.org/)
+- [matplotlib](https://matplotlib.org/)
+- [seaborn](https://seaborn.pydata.org/)
+
+### Installation from PyPI
+
+The easiest and recommended way to install ``PyRCN`` is to use ``pip`` from [PyPI](https://pypi.org) :
+
+```
+pip install pyrcn
+```
+
+### Installation from source
+
+If you plan to contribute to ``PyRCN``, you can also install the package from source.
+
+Clone the Git repository:
+
+```
+git clone https://github.com/TUD-STKS/PyRCN.git
+```
+
+Install the package using ``setup.py``:
+```
+python setup.py install --user
+```
+
+## Offcial documentation
+
+See [the official PyRCN documentation](https://pyrcn.readthedocs.io/en/latest/?badge=latest)
+to learn more about the main features of PyRCN, its API and the installation process.
+
+## Package structure
+The package is structured in the following way:
+
+- `doc`
+ - This folder includes the package documentation.
+- `examples`
+ - This folder includes example code as Jupyter Notebooks and python scripts.
+- `images`
+ - This folder includes the logos used in ´README.md´.
+- `pyrcn`
+ - This folder includes the actual Python package.
+
+
+## Getting Started
+
+PyRCN includes currently variants of Echo State Networks (ESNs) and Extreme Learning Machines (ELMs): Regressors and Classifiers.
+
+Basic example for the ESNClassifier:
+
+```python
+from sklearn.model_selection import train_test_split
+from pyrcn.datasets import load_digits
+from pyrcn.echo_state_network import ESNClassifier
+
+X, y = load_digits(return_X_y=True, as_sequence=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+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.datasets import mackey_glass
+from pyrcn.echo_state_network import ESNRegressor
+
+X, y = mackey_glass(n_timesteps=20000)
+
+reg = ESNRegressor()
+reg.fit(X=X[:8000], y=y[:8000])
+
+y_pred = reg.predict(X[8000:]) # output is the prediction for each input example
+```
+
+An extensive introduction to getting started with PyRCN is included in the [examples](https://github.com/TUD-STKS/PyRCN/blob/main/examples) directory.
+The notebook [digits](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/digits.py) show how to set up an ESN for a small hand-written digit recognition experiment.
+Launch the digits notebook on Binder:
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fdigits.ipynb)
+
+The notebook [PyRCN_Intro](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.ipynb) or its corresponding [Python script](https://github.com/TUD-STKS/PyRCN/blob/main/examples/PyRCN_Intro.py) show how to construct different RCNs with building blocks.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2FPyRCN_Intro.ipynb)
+
+The notebook [Impulse responses](https://github.com/TUD-STKS/PyRCN/blob/main/examples/esn_impulse_responses.ipynb) is an interactive tool to demonstrate the impact of different hyper-parameters on the impulse responses of an ESN.
+
+[](https://mybinder.org/v2/gh/TUD-STKS/PyRCN/main?filepath=examples%2Fesn_impulse_responses.ipynb)
+
+Fore more advanced examples, please have a look at our [Automatic Music Transcription Repository](https://github.com/TUD-STKS/Automatic-Music-Transcription), in which we provide an entire feature extraction, training and test pipeline for multipitch tracking and for note onset detection using PyRCN. This is currently transferred to this repository.
+
+## Citation
+
+If you use PyRCN, please cite the following publication:
+
+```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}
+}
+```
+
+## References
+
+[Unsupervised Pretraining of Echo State Networks for Onset Detection](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21e,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Igor Farka{\v{s}} and Paolo Masulli and Sebastian Otte and Stefan Wermter",
+ title="{U}nsupervised {P}retraining of {E}cho {S}tate {N}etworks for {O}nset {D}etection",
+ booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2021",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="59--70",
+ isbn="978-3-030-86383-8"
+}
+
+
+```
+
+[Improved Acoustic Modeling for Automatic Piano Music Transcription Using Echo State Networks](https://link.springer.com/chapter/10.1007/978-3-030-85099-9_12)
+
+```
+@InProceedings{src:Steiner-21d,
+ author="Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz",
+ editor="Ignacio Rojas and Gonzalo Joya and Andreu Catala",
+ title="{I}mproved {A}coustic {M}odeling for {A}utomatic {P}iano {M}usic {T}ranscription {U}sing {E}cho {S}tate {N}etworks",
+ booktitle="Advances in Computational Intelligence",
+ year="2021",
+ publisher="Springer International Publishing",
+ address="Cham",
+ pages="143--154",
+ isbn="978-3-030-85099-9"
+}
+```
+
+Glottal Closure Instant Detection using Echo State Networks
+- [Paper](http://www.essv.de/pdf/2021_161_168.pdf)
+- [Repository](https://github.com/TUD-STKS/gci_estimation)
+- Reference
+```latex
+@InProceedings{src:Steiner-21c,
+ title = {Glottal Closure Instant Detection using Echo State Networks},
+ author = {Peter Steiner and Ian S. Howard and Peter Birkholz},
+ year = {2021},
+ pages = {161--168},
+ keywords = {Oral},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2021},
+ editor = {Stefan Hillmann and Benjamin Weiss and Thilo Michael and Sebastian Möller},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-95908-227-3}
+}
+```
+
+Cluster-based Input Weight Initialization for Echo State Networks
+
+```latex
+@article{Steiner2022cluster,
+ author = {Steiner, Peter and Jalalvand, Azarakhsh and Birkholz, Peter},
+ doi = {10.1109/TNNLS.2022.3145565},
+ issn = {2162-2388},
+ journal = {IEEE Transactions on Neural Networks and Learning Systems},
+ keywords = {},
+ month = {},
+ number = {},
+ pages = {1--12},
+ title = {Cluster-based Input Weight Initialization for Echo State Networks},
+ volume = {},
+ year = {2022},
+}
+```
+
+PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks
+
+```latex
+@article{Steiner2022pyrcn,
+ title = {PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks},
+ journal = {Engineering Applications of Artificial Intelligence},
+ volume = {113},
+ pages = {104964},
+ year = {2022},
+ issn = {0952-1976},
+ doi = {10.1016/j.engappai.2022.104964},
+ url = {https://www.sciencedirect.com/science/article/pii/S0952197622001713},
+ author = {Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+}
+```
+
+[Feature Engineering and Stacked ESNs for Musical Onset Detection](https://ieeexplore.ieee.org/abstract/document/9413205)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21b,
+ author={Peter Steiner and Azarakhsh Jalalvand and Simon Stone and Peter Birkholz},
+ booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+ title={{F}eature {E}ngineering and {S}tacked {E}cho {S}tate {N}etworks for {M}usical {O}nset {D}etection},
+ year={2021},
+ volume={},
+ number={},
+ pages={9537--9544},
+ doi={10.1109/ICPR48806.2021.9413205}
+}
+```
+
+[Multipitch tracking in music signals using Echo State Networks](https://ieeexplore.ieee.org/abstract/document/9287638)
+
+```latex
+@INPROCEEDINGS{src:Steiner-21a,
+ author={Peter Steiner and Simon Stone and Peter Birkholz and Azarakhsh Jalalvand},
+ booktitle={2020 28th European Signal Processing Conference (EUSIPCO)},
+ title={{M}ultipitch tracking in music signals using {E}cho {S}tate {N}etworks},
+ year={2021},
+ volume={},
+ number={},
+ pages={126--130},
+ keywords={},
+ doi={10.23919/Eusipco47968.2020.9287638},
+ ISSN={2076-1465},
+ month={Jan},
+```
+
+[Note Onset Detection using Echo State Networks](http://www.essv.de/pdf/2020_157_164.pdf)
+
+```latex
+@InProceedings{src:Steiner-20,
+ title = {Note Onset Detection using Echo State Networks},
+ author = {Peter Steiner and Simon Stone and Peter Birkholz},
+ year = {2020},
+ pages = {157--164},
+ keywords = {Poster},
+ booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020},
+ editor = {Ronald Böck and Ingo Siegert and Andreas Wendemuth},
+ publisher = {TUDpress, Dresden},
+ isbn = {978-3-959081-93-1}
+}
+```
+
+[Multiple-F0 Estimation using Echo State Networks](https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf)
+
+```latex
+@inproceedings{src:Steiner-19,
+ title={{M}ultiple-{F}0 {E}stimation using {E}cho {S}tate {N}etworks},
+ booktitle={{MIREX}},
+ author={Peter Steiner and Azarakhsh Jalalvand and Peter Birkholz},
+ year={2019},
+ url = {https://www.music-ir.org/mirex/abstracts/2019/SBJ1.pdf}
+}
+```
+
+
+## Acknowledgments
+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.
+ + +%prep +%autosetup -n PyRCN-0.0.17.post1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-PyRCN -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.17.post1-1 +- Package Spec generated @@ -0,0 +1 @@ +07df487657800c6392ae02ed1a3f10fb PyRCN-0.0.17.post1.tar.gz |