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‎.github/workflows/python-package.yml

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python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies

‎.gitignore

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# VS Code
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.vscode
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.pylintrc
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# default pytest cache directory
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‎CHANGELOG

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Changes in SysIdentPy
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=====================
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v0.3.4
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------
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CONTRIBUTORS
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~~~~~~~~~~~~
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- wilsonrljr
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- dj-gauthier
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CHANGES
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~~~~~~~
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- The update **v0.3.4** has been released with additional features, API changes and fixes.
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- MAJOR: Ridge Regression Parameter Estimation:
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- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
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- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
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- See the docs for a more in depth explanation of how to use AILS.
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- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
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- API Change: plotting.py code was improved. Added type hints and added new options for plotting results.
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- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.
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- DOC: Add a Multiobjective Parameter Optimization Notebook showing how to use the new AILS method
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- DOC: Minor additions and grammar fixes.
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v0.3.3
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------
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CONTRIBUTORS
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~~~~~~~~~~~~
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- wilsonrljr
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- gamcorn
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- Gabo-Tor
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CHANGES
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~~~~~~~
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- The update **v0.3.3** has been released with additional features, API changes and fixes.
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- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
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- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
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- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
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- See the docs for a more in depth explanation of how to use AILS.
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- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
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- API Change: `regressor_code` variable was renamed as `encoding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.
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- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.
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- DOC: Add a Multiobjective Parameter Optimization Notebook showing how to use the new AILS method
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- DOC: Minor additions and grammar fixes.
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‎README.md

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<p align="center">
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<div align="center">
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<img src="images/sysidentpy-logo.svg" width="640" height="320" />
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</p>
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[![DOI](https://img.shields.io/badge/DOI-10.21105%2Fjoss.02384-%23FF7800)](https://joss.theoj.org/papers/10.21105/joss.02384)
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[![PyPI version](https://img.shields.io/pypi/v/sysidentpy?color=%23ff7800)](https://pypi.org/project/sysidentpy/)
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[![status](https://img.shields.io/pypi/status/sysidentpy?color=%23FF7800)](https://pypi.org/project/sysidentpy/)
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[![discord](https://img.shields.io/discord/711610087700955176?color=%23FF7800&label=discord)](https://discord.gg/7afBSzU4)
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[![contributors](https://img.shields.io/github/contributors/wilsonrljr/sysidentpy?color=%23FF7800)](https://github.com/wilsonrljr/sysidentpy/graphs/contributors)
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[![forks](https://img.shields.io/github/forks/wilsonrljr/sysidentpy?style=social)](https://github.com/wilsonrljr/sysidentpy/network/members)
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[![stars](https://img.shields.io/github/stars/wilsonrljr/sysidentpy?style=social)](https://github.com/wilsonrljr/sysidentpy/stargazers)
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<h1 align="center"> NARMAX Methods In The Time Domain </h1>
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<h3 align="center">From Classical Approaches to Neural Networks</h3>
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**SysIdentPy** offers State of the Art techniques to build your NARMAX models, including it's variants `NARX`, `NARMA`, `NAR`, `NFIR`, `ARMAX`, `ARX`, `ARMA` and others. It also includes tons of interesting examples to help you build nonlinear forecasting models using SysIdentPy.
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</div>
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## What is SysIdentPy?
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### Requirements
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SysIdentPy requires:
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`SysIdentPy` requires:
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- Python (>= 3.7)
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- NumPy (>= 1.9.2) for all numerical algorithms
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| Model Structure Selection | Easy-to-use methods to select the best terms to build your models, including FROLS and MetaMSS and several combinations with parameter estimation techniques to select the model terms. |
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| Basis Function | You can use different basis functions to build your models. You can set linear and nonlinear basis functions and ensemble them to get custom NARMAX models. |
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| Parameter Estimation | More than 15 methods to estimate the model parameters and test different structure selection scenarios. |
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| Multiobjective Parameter Estimation | You can use affine information to estimate the model parameters minimizing different objective functions. |
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| Model Simulation | You can reproduce results from papers easily with SimulateNARMAX class. Moreover, you can test published models with different parameter estimation methods and compare the performance. |
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| Neural NARX | You can use SysIdentPy with Pytorch to create custom neural NARX models architectures which support all the optimizers and loss functions from Pytorch. |
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| General Estimators | You can use estimators from packages like scikit-learn, Catboost, and many other compatible interfaces and composition tools, to create NARMAX models. |
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## How do I use sysIdentPy?
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The [SysIdentPy documentation](https://sysidentpy.org) includes 20 examples to help get you started:
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The [SysIdentPy documentation](https://sysidentpy.org) includes more than 20 examples to help get you started:
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- Typical "Hello World" example, for an [entry-level description of the main SysIdentPy concepts](https://sysidentpy.org/examples/basic_steps/)
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- A dedicated section focusing on SysIdentPy features, like model structure selection algorithms, basis functions, parameter estimation, and more.
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- A dedicated section focusing on use cases using SysIdentPy with real world datasets. Besides, there is some brief comparisons and benchmarks against other time series tools, like Prophet, Neural Prophet, ARIMA, and more.

‎docs/book/contents/index.html

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‎docs/book/preface.md

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## Contribute
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The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
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The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
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## Note
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The chapters will be released one by one until the book is complete.

‎docs/book/preface/index.html

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## Contribute
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The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
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The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
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## Note
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The chapters will be released one by one until the book is complete.

‎docs/changelog/changelog.md

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# Changes in SysIdentPy
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## v0.3.3
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### CONTRIBUTORS
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- wilsonrljr
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- GabrielBuenoLeandro
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- samirmartins
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### CHANGES
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- The update **v0.3.3** has been released with additional features, API changes and fixes.
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- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
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- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
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- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
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- See the docs for a more in depth explanation of how to use AILS.
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- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
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- Several new methods were implemented to get the new feature and you can check all of it in sysidentpy -> multiobjective_parameter_estimation.
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- API Change: `regressor_code` variable was renamed as `enconding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.
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- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.
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- DOC: Add a Multiobjetive Parameter Optimization Notebook showing how to use the new AILS method
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- DOC: Minor additions and grammar fixes.
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## v0.3.2
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‎docs/changelog/changelog/changelog.md

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# Changes in SysIdentPy
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## v0.3.3
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### CONTRIBUTORS
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- wilsonrljr
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- GabrielBuenoLeandro
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- samirmartins
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### CHANGES
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- The update **v0.3.3** has been released with additional features, API changes and fixes.
19+
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- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
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- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
22+
- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
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- See the docs for a more in depth explanation of how to use AILS.
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- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
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- Several new methods were implemented to get the new feature and you can check all of it in sysidentpy -> multiobjective_parameter_estimation.
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- API Change: `regressor_code` variable was renamed as `enconding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.
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- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.
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- DOC: Add a Multiobjetive Parameter Optimization Notebook showing how to use the new AILS method
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- DOC: Minor additions and grammar fixes.
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## v0.3.2
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### CONTRIBUTORS

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