Welcome to EvoRBF’s documentation!

EvoRBF: Evolving Radial Basis Function Network by Intelligent Nature-inspired Algorithms

EvoRBF (Evolving Radial Basis Function Network) is a Python library that implements a framework for training Radial Basis Function (RBF) networks using Intelligence Nature-inspired Algorithms (INAs). 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, InaRbfRegressor, InaRbfClassifier, InaRbfTuner

• 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

Quick Start:

• Installation
• Examples

Models API:

• EvoRBF Library
  • evorbf.core package
    • evorbf.core.base_rbf module
    • evorbf.core.ina_rbf module
    • evorbf.core.rbf_tuner module
    • evorbf.core.standard_rbf module
  • evorbf.helpers package
    • evorbf.helpers.metrics module
    • evorbf.helpers.preprocessor module
    • evorbf.helpers.scaler module
    • evorbf.helpers.validator module

Support:

• Citation Request
• Important links
• License

Indices and tables

• Index

• Module Index

• Search Page