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Welcome to EvoRBF's documentation!
==================================

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**EvoRBF: A Nature-inspired Algorithmic Framework for Evolving Radial Basis Function Networks**

EvoRBF (Evolving Radial Basis Function Network) is a Python library that implements a framework
for training Radial Basis Function (RBF) networks using `Nature-inspired Algorithms (NIAs)`. It provides a
comparable alternative to the traditional RBF network and is compatible with the Scikit-Learn library. With EvoRBF, you can
perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.

* **Free software:** GNU General Public License (GPL) V3 license
* **Provided Estimator**: RbfRegressor, RbfClassifier, AdvancedRbfRegressor, AdvancedRbfClassifier, NiaRbfRegressor, NiaRbfClassifier, NiaRbfTuner
* **Total InaRBf models**: > 400 Models
* **Supported performance metrics**: >= 67 (47 regressions and 20 classifications)
* **Supported loss functions**: >= 61 (45 regressions and 16 classifications)
* **Documentation:** https://evorbf.readthedocs.io/en/latest/
* **Python versions:** >= 3.8.x
* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics


.. toctree::
   :maxdepth: 4
   :caption: Quick Start:

   pages/quick_start.rst

.. toctree::
   :maxdepth: 4
   :caption: Models API:

   pages/evorbf.rst

.. toctree::
   :maxdepth: 4
   :caption: Support:

   pages/support.rst



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

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