import unittest
import math

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestUserCode(unittest.TestCase):

    def test_fuzzy_and_min(self):
        import user_code
        expected = 0.5
        actual = user_code.fuzzy_and_result
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The fuzzy AND result using the minimum operator is correct.",
            f"Expected `fuzzy_and_result` to be {expected}, but got {actual}."
        )

    def test_fuzzy_or_max(self):
        import user_code
        expected = 0.7
        actual = user_code.fuzzy_or_result
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The fuzzy OR result using the maximum operator is correct.",
            f"Expected `fuzzy_or_result` to be {expected}, but got {actual}."
        )

    def test_fuzzy_and_product(self):
        import user_code
        expected = 0.35
        actual = user_code.fuzzy_and_product
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The fuzzy AND result using the product t-norm is correct.",
            f"Expected `fuzzy_and_product` to be {expected}, but got {actual}."
        )

if __name__ == "__main__":
    unittest.main()

test_main.py

Explore fuzzy logic as a framework for reasoning under vagueness. Learn fuzzy sets, membership functions, fuzzy operators, and rule-based inference with clear theory and concise NumPy examples.

Discover the limitations of classical logic and the motivation for fuzzy logic. Learn how fuzzy sets and degrees of truth enable reasoning under vagueness.

Model vagueness numerically using membership functions. Learn to design, visualize, and interpret fuzzy sets.

Delve into fuzzy logical operators, t-norms, t-conorms, and how fuzzy rules are composed.

Integrate fuzzy rules into inference systems and perform approximate reasoning.

Challenge: Combining Fuzzy Conditions

Instructions

Ratkaisu

Challenge: Combining Fuzzy Conditions

Instructions

Ratkaisu