Welcome to WaveletML’s documentation!¶

WaveletML is an open-source Python framework designed for building, training, and evaluating Wavelet Neural Networks (WNNs) tailored for supervised learning tasks such as regression and classification. Leveraging the power of PyTorch and the modularity of scikit-learn, WaveletML provides a unified, extensible, and scalable platform for researchers and practitioners to explore wavelet-based neural architectures.

• Free software: GNU General Public License (GPL) V3 license

• Provided Estimators: GdWnnClassifier, GdWnnRegressor, MhaWnnClassifier, MhaWnnRegressor

• Supported Wavelet Functions: Morlet, Mexican Hat, Haar, …

• Supported Wavelet Layers: - Weighed Linear Layer - Product Layer - Summation Layer

• Supported performance metrics: >= 67 (47 regressions and 20 classifications)

• Documentation: https://waveletml.readthedocs.io

• Python versions: >= 3.8.x

• Dependencies: numpy, scipy, scikit-learn, pandas, permetrics, mealpy, torch

Indices and tables¶

• Index

• Module Index

• Search Page