Modern Python package of the Adaptive Filtering Toolbox
Updated 18 Jan 2023


A modern Python package of adaptive filtering functions based on the book Adaptive Filtering - Algorithms and Pratical Implementation, Paulo Diniz. Also, based on the MATLAB Adaptive Filtering Toolbox.

Future Work

The project is currently on its early stages (pre-alpha). The planned order of work for every kind of algorithm is: (Algorithm(1), Examples(2), Notebooks(3)). The following is the planned and current progress for each of the algorithms:

  • LMS based algorithms
  • RLS based algorithms
  • SetMembership Algorithms
  • Lattice-based RLS
  • Fast Transversal RLS
  • QR
  • IIR Filters (1/5)
  • Nonlinear Filters (0/6)
  • Subband Filters (0/3)
  • BlindFilters

💻 Requirements

  • Currently it requires Python 3.5 or later.

🚀 Install Instructions

To install the package with pip:

pip install pydaptivefiltering

To install the package with conda:

conda install pydaptivefiltering

☕ Examples of uses

A good place to start is with the jupyter notebooks that can be found at the <Examples\Jupyter Notebooks> folder

import pydaptivefiltering as pdf

📝 License

This project is under the license found at LICENSE.

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Cite As

Paulo S. R. Diniz (2024). PydaptiveFiltering (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Versions that use the GitHub default branch cannot be downloaded

Version Published Release Notes

In construction.


Fifth Edition of the Adaptive Filtering Book


Authors of the MATLAB files are now listed.

Improving format.

Cover replacement

Book cover.

This update allows to use the Adaptive Filtering Program as a Toolbox.

The equation-error and Steiglitz-McBride algorithms were changed.

Just placed author names in the same order everywhere,

Include all authors.

These codes substitute the codes from the
second edition.

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.