import unittest
import numpy as np
import importlib
import math

# --- Constants for Testing ---
# We know the true minimum is approx -3.51
# We will check that the algorithm finds a solution better than -3.0
OPTIMALITY_THRESHOLD = -3.0

def _dynamic_test(test_case, condition, success_message, failure_message):
    """Helper function to dynamically pass/fail tests."""
    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 TestPSOSimulation(unittest.TestCase):
    
    @classmethod
    def setUpClass(cls):
        """Importing the user's code and running the PSO."""
        global user_code
        try:
            user_code = importlib.import_module("user_code")
            # Running the PSO to get the results for testing
            cls.best_x, cls.best_value = user_code.run_pso()
        except ImportError:
            cls.fail("Could not import user_code.py. Make sure the file exists.")
        except Exception as e:
            cls.fail(f"Error running run_pso(): {e}")

    def test_return_types(self):
        """Checking if the PSO returns values with the correct types."""
        # Checking best_x type
        condition_x = isinstance(self.best_x, (float, np.float64))
        _dynamic_test(
            self,
            condition_x,
            "The returned `best_position` is a float.",
            f"The returned `best_position` should be a float, but got {type(self.best_x)}."
        )
        
        # Checking best_value type
        condition_f = isinstance(self.best_value, (float, np.float64))
        _dynamic_test(
            self,
            condition_f,
            "The returned `best_value` is a float.",
            f"The returned `best_value` should be a float, but got {type(self.best_value)}."
        )

    def test_fitness_function_correctness(self):
        """Checking if the fitness_function calculates a known value correctly."""
        # This test checks the helper function 'f' directly
        test_val = user_code.f(math.pi)
        expected_val = math.pi ** 2 + 5 * math.sin(math.pi) # sin(pi) is 0
        
        condition = np.isclose(test_val, math.pi ** 2)
        _dynamic_test(
            self,
            condition,
            "The `f(pi)` function correctly returns pi^2.",
            f"The `f(pi)` function returned {test_val}, but should be {math.pi ** 2}."
        )

    def test_solution_in_range(self):
        """Checking that the `best_position` is within the allowed bounds."""
        condition = user_code.xmin <= self.best_x <= user_code.xmax
        _dynamic_test(
            self,
            condition,
            f"The solution `best_position` ({self.best_x:.4f}) is within the [{user_code.xmin}, {user_code.xmax}] range.",
            f"The solution `best_position` ({self.best_x:.4f}) is outside the allowed [{user_code.xmin}, {user_code.xmax}] range."
        )
        
    def test_solution_is_consistent(self):
        """Checking that f(best_position) matches the reported `best_value`."""
        # This checks if the user's logic correctly paired the position and value
        calculated_value = user_code.f(self.best_x)
        condition = np.isclose(calculated_value, self.best_value)
        _dynamic_test(
            self,
            condition,
            "The reported `best_value` matches the result of `f(best_position)`.",
            f"The reported `best_value` ({self.best_value:.6f}) does not match `f(best_position)` ({calculated_value:.6f})."
        )

    def test_solution_is_optimal(self):
        """Checking that the algorithm found a good solution (value < -3.0)."""
        # This proves the optimization worked without being brittle
        condition = self.best_value < OPTIMALITY_THRESHOLD
        _dynamic_test(
            self,
            condition,
            f"The solution `best_value` ({self.best_value:.6f}) is less than the threshold ({OPTIMALITY_THRESHOLD}), indicating a successful optimization.",
            f"The solution `best_value` ({self.best_value:.6f}) is not less than the threshold ({OPTIMALITY_THRESHOLD}). The optimization may have failed."
        )

if __name__ == "__main__":
    unittest.main(argv=['first-arg-is-ignored'], exit=False)


test_main.py

Explora los fundamentos y las aplicaciones prácticas de los algoritmos bioinspirados en Python. Este curso abarca computación evolutiva, inteligencia de enjambre y otras técnicas de optimización inspiradas en la naturaleza, utilizando únicamente Python estándar y bibliotecas científicas permitidas.

Comprender la motivación, la historia y los principios fundamentales detrás de la computación bioinspirada, incluyendo evolución, adaptación e inteligencia de enjambre.

Explora la estructura, los operadores y el ajuste de parámetros de los algoritmos genéticos.

Investigar algoritmos inspirados en el comportamiento colectivo en la naturaleza, incluyendo hormigas, partículas y otros enjambres.

Explora los principios y algoritmos inspirados en el sistema inmunológico biológico, incluyendo la selección negativa y la selección clonal.

Desafío: Implementar Optimización por Enjambre de Partículas

Solución