def test__random_selection(self): parent = [np.array([1, 2, 3, 4, 5, 6]), np.array([7, 8, 9, 10, 11, 12]), np.array([13, 14, 15, 16, 17, 18])] # seeded random uniform array expected = [np.array([1, 2, 3, 4, 5, 6]), np.array([7, 8, 9, 10, 11, 12])] inverter, _ = init_default_test_inverter() # when actual = inverter._GAMLPInverter__random_selection(1, parent) # then np.testing.assert_allclose(actual, expected)
def test_seeded_inversion_exp(self): # given inverter, regressor = init_default_test_inverter() true_y = np.mean([n for n in range(1, 9)])**2 inverted = inverter.invert([true_y]) # when inverted_y = regressor.predict(inverted) # then self.assertEqual(check_inverted_y(inverted_y, true_y), True)
def test__one_point_crossover_assert(self): # given parent1 = np.array([1, 2, 3, 4]) parent2 = np.array([5, 6, 7, 8]) expected = np.array([1, 2, 7, 8]) inverter, _ = init_default_test_inverter() # when actual = inverter._GAMLPInverter__one_point_crossover(parent1, parent2) # then np.testing.assert_allclose(actual, expected)
def test__arithmetic_crossover(self): # given parent1 = np.array([1, 2, 3, 4, 5, 6]) parent2 = np.array([5, 6, 7, 8, 9, 10]) # seeded random uniform array expected = np.array([3, 4, 5, 6, 7, 8]) inverter, _ = init_default_test_inverter() # when actual = inverter._GAMLPInverter__arithmetic_crossover(parent1, parent2) # then np.testing.assert_allclose(actual, expected)
def test_init_invert(self): # given, when inverter, _ = init_default_test_inverter() # then self.assertIsInstance(inverter, GAMLPInverter)