fuzzy_inference/example.py at main · TheLion-ai/fuzzy_inference · GitHub

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"""
Example: Fuzzy Logic Tipping System

This example demonstrates a fuzzy logic system for calculating restaurant tips
based on food quality and service quality.
"""

from fuzzy_inference import FuzzyVariable, FuzzySet, FuzzyRule, FuzzySystem
from fuzzy_inference import triangular, trapezoidal


def main():
    # Create input variables
    food_quality = FuzzyVariable("food_quality", 0, 10)
    service_quality = FuzzyVariable("service_quality", 0, 10)

    # Create output variable
    tip = FuzzyVariable("tip", 0, 25)

    # Define fuzzy sets for food quality
    food_quality.add_set(FuzzySet("poor", lambda x: triangular(x, 0, 0, 5)))
    food_quality.add_set(FuzzySet("good", lambda x: triangular(x, 0, 5, 10)))
    food_quality.add_set(FuzzySet("excellent", lambda x: triangular(x, 5, 10, 10)))

    # Define fuzzy sets for service quality
    service_quality.add_set(FuzzySet("poor", lambda x: triangular(x, 0, 0, 5)))
    service_quality.add_set(FuzzySet("good", lambda x: triangular(x, 0, 5, 10)))
    service_quality.add_set(FuzzySet("excellent", lambda x: triangular(x, 5, 10, 10)))

    # Define fuzzy sets for tip
    tip.add_set(FuzzySet("low", lambda x: triangular(x, 0, 0, 13)))
    tip.add_set(FuzzySet("medium", lambda x: triangular(x, 0, 13, 25)))
    tip.add_set(FuzzySet("high", lambda x: triangular(x, 13, 25, 25)))

    # Create fuzzy system
    system = FuzzySystem()
    system.add_input_variable(food_quality)
    system.add_input_variable(service_quality)
    system.add_output_variable(tip)

    # Define rules
    rules = [
        # If food is poor OR service is poor, tip is low
        FuzzyRule(
            {"food_quality": food_quality["poor"], "service_quality": service_quality["poor"]},
            {"tip": tip["low"]}
        ),
        FuzzyRule(
            {"food_quality": food_quality["poor"], "service_quality": service_quality["good"]},
            {"tip": tip["low"]}
        ),
        FuzzyRule(
            {"food_quality": food_quality["good"], "service_quality": service_quality["poor"]},
            {"tip": tip["low"]}
        ),
        # If food is good AND service is good, tip is medium
        FuzzyRule(
            {"food_quality": food_quality["good"], "service_quality": service_quality["good"]},
            {"tip": tip["medium"]}
        ),
        # If food is excellent OR service is excellent, tip is high
        FuzzyRule(
            {"food_quality": food_quality["excellent"], "service_quality": service_quality["excellent"]},
            {"tip": tip["high"]}
        ),
        FuzzyRule(
            {"food_quality": food_quality["excellent"], "service_quality": service_quality["good"]},
            {"tip": tip["medium"]}
        ),
        FuzzyRule(
            {"food_quality": food_quality["good"], "service_quality": service_quality["excellent"]},
            {"tip": tip["medium"]}
        ),
    ]

    for rule in rules:
        system.add_rule(rule)

    # Test the system with different inputs
    test_cases = [
        {"food_quality": 3, "service_quality": 3},
        {"food_quality": 7, "service_quality": 7},
        {"food_quality": 9, "service_quality": 9},
        {"food_quality": 2, "service_quality": 8},
        {"food_quality": 8, "service_quality": 2},
    ]

    print("Fuzzy Logic Tipping System")
    print("=" * 50)

    for inputs in test_cases:
        result = system.infer(inputs)
        print(f"\nInput: Food Quality = {inputs['food_quality']}, "
              f"Service Quality = {inputs['service_quality']}")
        print(f"Output: Tip = {result['tip']:.2f}%")

    # Demonstrate fuzzification
    print("\n" + "=" * 50)
    print("Fuzzification Example:")
    print("=" * 50)
    food_value = 6.5
    print(f"\nFood quality value: {food_value}")
    print("Membership in fuzzy sets:")
    for set_name, membership_value in food_quality.fuzzify(food_value).items():
        print(f"  {set_name}: {membership_value:.3f}")


if __name__ == "__main__":
    main()