pyfuzzylite 8.0.1

A Fuzzy Logic Control Library in Python

by Juan Rada-Vilela, PhD

main branch

development branch

FuzzyLite

The FuzzyLite Libraries for Fuzzy Logic Control refer to fuzzylite (C++), pyfuzzylite (Python), and jfuzzylite (Java).

The goal of the FuzzyLite Libraries is to easily design and efficiently operate fuzzy logic controllers following an object-oriented programming model with minimal dependency on external libraries.

License

pyfuzzylite is dual-licensed under the GNU GPL 3.0 and under a proprietary license for commercial purposes.

You are strongly encouraged to support the development of the FuzzyLite Libraries by purchasing a license of QtFuzzyLite.

QtFuzzyLite is the best graphical user interface available to easily design and directly operate fuzzy logic controllers in real time. Available for Windows, Mac, and Linux, its goal is to significantly speed up the design of your fuzzy logic controllers, while providing a very useful, functional and beautiful user interface. Please, download it and check it out for free at fuzzylite.com/downloads.

Install

pip install pyfuzzylite

Features

Documentation: fuzzylite.github.io/pyfuzzylite/

(6) Controllers: Mamdani, Takagi-Sugeno, Larsen, Tsukamoto, Inverse Tsukamoto, Hybrid

(25) Linguistic terms: (5) Basic: Triangle, Trapezoid, Rectangle, Discrete, SemiEllipse. (8) Extended: Bell, Cosine, Gaussian, GaussianProduct, PiShape, SigmoidDifference, SigmoidProduct, Spike. (7) Edges: Arc, Binary, Concave, Ramp, Sigmoid, SShape, ZShape. (3) Functions: Constant, Linear, Function. (2) Special: Aggregated, Activated.

(7) Activation methods: General, Proportional, Threshold, First, Last, Lowest, Highest.

(9) Conjunction and Implication (T-Norms): Minimum, AlgebraicProduct, BoundedDifference, DrasticProduct, EinsteinProduct, HamacherProduct, NilpotentMinimum, LambdaNorm, FunctionNorm.

(11) Disjunction and Aggregation (S-Norms): Maximum, AlgebraicSum, BoundedSum, DrasticSum, EinsteinSum, HamacherSum, NilpotentMaximum, NormalizedSum, UnboundedSum, LambdaNorm, FunctionNorm.

(7) Defuzzifiers: (5) Integral: Centroid, Bisector, SmallestOfMaximum, LargestOfMaximum, MeanOfMaximum. (2) Weighted: WeightedAverage, WeightedSum.

(7) Hedges: Any, Not, Extremely, Seldom, Somewhat, Very, Function.

(3) Importers: FuzzyLite Language fll. With fuzzylite: Fuzzy Inference System fis, Fuzzy Control Language fcl.

(7) Exporters: Python, FuzzyLite Language fll, FuzzyLite Dataset fld. With fuzzylite: C++, Java, FuzzyLite Language fll, FuzzyLite Dataset fld, R script, Fuzzy Inference System fis, Fuzzy Control Language fcl.

(30+) Examples of Mamdani, Takagi-Sugeno, Tsukamoto, and Hybrid controllers from fuzzylite, Octave, and Matlab, each included in the following formats: py, fll, fld. With fuzzylite: C++, Java, R, fis, and fcl.

Examples

FuzzyLite Language

# File: examples/mamdani/ObstacleAvoidance.fll
Engine: ObstacleAvoidance
InputVariable: obstacle
  enabled: true
  range: 0.000 1.000
  lock-range: false
  term: left Ramp 1.000 0.000
  term: right Ramp 0.000 1.000
OutputVariable: mSteer
  enabled: true
  range: 0.000 1.000
  lock-range: false
  aggregation: Maximum
  defuzzifier: Centroid 100
  default: nan
  lock-previous: false
  term: left Ramp 1.000 0.000
  term: right Ramp 0.000 1.000
RuleBlock: mamdani
  enabled: true
  conjunction: none
  disjunction: none
  implication: AlgebraicProduct
  activation: General
  rule: if obstacle is left then mSteer is right
  rule: if obstacle is right then mSteer is left

# Python
import fuzzylite as fl

engine = fl.FllImporter().from_file("examples/mamdani/ObstacleAvoidance.fll")

Python

import fuzzylite as fl

engine = fl.Engine(
    name="ObstacleAvoidance",
    input_variables=[
        fl.InputVariable(
            name="obstacle",
            minimum=0.0,
            maximum=1.0,
            lock_range=False,
            terms=[fl.Ramp("left", 1.0, 0.0), fl.Ramp("right", 0.0, 1.0)],
        )
    ],
    output_variables=[
        fl.OutputVariable(
            name="mSteer",
            minimum=0.0,
            maximum=1.0,
            lock_range=False,
            lock_previous=False,
            default_value=fl.nan,
            aggregation=fl.Maximum(),
            defuzzifier=fl.Centroid(resolution=100),
            terms=[fl.Ramp("left", 1.0, 0.0), fl.Ramp("right", 0.0, 1.0)],
        )
    ],
    rule_blocks=[
        fl.RuleBlock(
            name="mamdani",
            conjunction=None,
            disjunction=None,
            implication=fl.AlgebraicProduct(),
            activation=fl.General(),
            rules=[
                fl.Rule.create("if obstacle is left then mSteer is right"),
                fl.Rule.create("if obstacle is right then mSteer is left"),
            ],
        )
    ],
)

Contributing

All contributions are welcome, provided they follow the following guidelines:

• Pull requests are made to the development branch
• Source code is consistent with standards in the library
• Contribution is properly documented and tested, raising issues where appropriate
• Contribution is licensed under the FuzzyLite License

Reference

If you are using the FuzzyLite Libraries, please cite the following reference in your article:

Juan Rada-Vilela. The FuzzyLite Libraries for Fuzzy Logic Control, 2018. URL https://fuzzylite.com.

Or using bibtex:

@misc{fl::fuzzylite,
    author={Juan Rada-Vilela},
    title={The FuzzyLite Libraries for Fuzzy Logic Control},
    url={https://fuzzylite.com},
    year={2018}
}

fuzzylite® is a registered trademark of FuzzyLite Limited
jfuzzylite™ is a trademark of FuzzyLite Limited
pyfuzzylite™ is a trademark of FuzzyLite Limited
QtFuzzyLite™ is a trademark of FuzzyLite Limited