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)
