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_function_exists(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, "triangular_membership"),
            "The `triangular_membership` function is defined.",
            "Expected a function named `triangular_membership`."
        )

    def test_membership_low_value(self):
        import user_code
        expected = 0.4
        actual = user_code.membership_low
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The `membership_low` value is correct.",
            f"Expected `membership_low` to be {expected}, but got {actual}."
        )

    def test_membership_medium_value(self):
        import user_code
        expected = 1.0
        actual = user_code.membership_medium
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The `membership_medium` value is correct.",
            f"Expected `membership_medium` to be {expected}, but got {actual}."
        )

    def test_membership_high_value(self):
        import user_code
        expected = 0.4
        actual = user_code.membership_high
        _dynamic_test(
            self,
            math.isclose(actual, expected, rel_tol=1e-5),
            "The `membership_high` value is correct.",
            f"Expected `membership_high` to be {expected}, but got {actual}."
        )

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

test_code.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: Designing Membership Functions

Instructions

Ratkaisu

Challenge: Designing Membership Functions

Instructions

Ratkaisu