HotFIS

Create, alter, and visualize fuzzy inference systems in Python!

Description

HotFIS is a library designed to support fuzzy logic through the streamlined creation and evaluation of fuzzy inference systems (FIS) in Python.

In order to support machine learning and data science applications, the library leverages Numpy [1] to evaluate both scalar and array-like input via Mamdani [2] or Takagi-Sugeno [3] inference. Additionally, Matplotlib [4] is employed to quickly visualize output and support explainability.

Note: HotFIS is currently in early development! Any suggested features and improvements are welcome.

Features

• Creation of functions capable of determining membership to an implicitly defined fuzzy set
• Organization of function in groups with a domain for Mamdani evaluation and visualization
• Creation of fuzzy rules with one or more antecedents using natural language
• Deserialization of both membership functions and fuzzy rulesets
• Mamdani and Takagi-Sugeno-Kang inference with scalar and array-like inputs
• Visualization of membership functions, function groups, and fuzzfied Mamdani output
• Networks of multiple fuzzy inference systems
• Experimental conversion of Takagi-Sugeno output functions to Mamdani outputs for explainability

Installation

Currently, HotFIS is hosted on TestPyPI.

Installation with all dependencies:

pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple hotfis

Installation with only HotFIS:

pip install -i https://test.pypi.org/simple/ hotfis

Dependencies

HotFIS depends on:

• Numpy
• Matplotlib

References

[1]	C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020, doi: 10.1038/s41586-020-2649-2.

[2]	E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, Jan. 1975, doi: 10.1016/s0020-7373(75)80002-2.

[3]	T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116-132, Jan.-Feb. 1985, doi: 10.1109/TSMC.1985.6313399.

[4]	J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007, doi: 10.1109/mcse.2007.55.

[5]	M. Madina, "Intepretable learning of Takagi-Sugeno fuzzy systems and application to improving predictions of the El Nino southern oscillation," M.S. thesis, DigiPen Institute of Technology.

Documentation

Documentation can be found at:

• https://ericzander.github.io/hotfis