def test_none_nested_list(self):
"""
Test the processing of a nested list that is already normalized.
"""
obtained_z_matrix, obtained_is_benefit_z = normalize(
get_matrix01(),
[True, True, True],
None,
)
self.assertAlmostEqualArrays(
obtained_z_matrix,
np.array(get_matrix01(), dtype=np.float64),
)
self.assertEqual(obtained_is_benefit_z, [True, True, True])
def test_vector_float32(self):
"""Test the Vector method with a float32 NumPy array."""
x_matrix = np.array([[0.0, 0.0, 5.0, 5.0], [6.0, 6.0, 5.0, 5.0],
[0.0, 0.0, 5.0, 5.0], [8.0, 8.0, 5.0, 5.0]],
dtype=np.float32)
is_benefit_x = [True, False, True, False]
obtained_z_matrix, obtained_is_benefit_z = mcdm.normalize(
x_matrix, is_benefit_x, "Vector")
expected_z_matrix = np.array(
[[0.0, 0.0, 0.5, 0.5], [0.6, 0.6, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5],
[0.8, 0.8, 0.5, 0.5]],
dtype=np.float64)
expected_is_benefit_z = [True, False, True, False]
np.testing.assert_allclose(obtained_z_matrix, expected_z_matrix)
self.assertEqual(obtained_z_matrix.dtype, expected_z_matrix.dtype)
self.assertEqual(obtained_is_benefit_z, expected_is_benefit_z)
def test_linear3_float32(self):
"""Test the Linear3 method with a float32 NumPy array."""
x_matrix = np.array(
[[4.0, 4.0, 7.0, 7.0], [3.0, 3.0, 7.0, 7.0], [2.0, 2.0, 7.0, 7.0],
[1.0, 1.0, 7.0, 7.0], [0.0, 0.0, 7.0, 7.0]],
dtype=np.float32)
is_benefit_x = [True, False, True, False]
obtained_z_matrix, obtained_is_benefit_z = mcdm.normalize(
x_matrix, is_benefit_x, "Linear3")
expected_z_matrix = np.array(
[[0.4, 0.4, 0.2, 0.2], [0.3, 0.3, 0.2, 0.2], [0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.2], [0.0, 0.0, 0.2, 0.2]],
dtype=np.float64)
expected_is_benefit_z = [True, False, True, False]
np.testing.assert_allclose(obtained_z_matrix, expected_z_matrix)
self.assertEqual(obtained_z_matrix.dtype, expected_z_matrix.dtype)
self.assertEqual(obtained_is_benefit_z, expected_is_benefit_z)
def test_none_float32(self):
"""Test a float32 NumPy array that is already normalized."""
x_matrix = np.array([[0.0, 0.0, 1.0], [0.1, 0.2, 0.8], [0.2, 0.4, 0.6],
[0.3, 0.7, 0.3], [0.6, 0.8, 0.2], [0.8, 0.9, 0.1],
[1.0, 1.0, 0.0]],
dtype=np.float32)
is_benefit_x = [True, True, True]
obtained_z_matrix, obtained_is_benefit_z = mcdm.normalize(
x_matrix, is_benefit_x, None)
expected_z_matrix = np.array(
[[0.0, 0.0, 1.0], [0.1, 0.2, 0.8], [0.2, 0.4, 0.6], [
0.3, 0.7, 0.3
], [0.6, 0.8, 0.2], [0.8, 0.9, 0.1], [1.0, 1.0, 0.0]],
dtype=np.float64)
expected_is_benefit_z = [True, True, True]
np.testing.assert_allclose(obtained_z_matrix, expected_z_matrix)
self.assertEqual(obtained_z_matrix.dtype, expected_z_matrix.dtype)
self.assertEqual(obtained_is_benefit_z, expected_is_benefit_z)
def test_linear2_nested_list(self):
"""Test the Linear2 method with a nested list."""
x_matrix = [
[8.0, 8.0, -1.0, -1.0, 5.0, 5.0], # noqa: E201
[24.0, 24.0, -11.0, -11.0, 0.0, 0.0], # noqa: E201
[4.0, 4.0, -10.0, -10.0, 40.0, 40.0], # noqa: E201
[14.0, 14.0, -9.0, -9.0, 15.0, 15.0], # noqa: E201
[6.0, 6.0, -7.0, -7.0, -5.0, -5.0], # noqa: E201
[18.0, 18.0, -5.0, -5.0, -10.0, -10.0], # noqa: E201
]
is_benefit_x = [True, False, True, False, True, False]
obtained_z_matrix, obtained_is_benefit_z = mcdm.normalize(
x_matrix, is_benefit_x, "Linear2")
expected_z_matrix = np.array(
[[0.2, 0.8, 1.0, 0.0, 0.3, 0.7], [1.0, 0.0, 0.0, 1.0, 0.2, 0.8],
[0.0, 1.0, 0.1, 0.9, 1.0, 0.0], [0.5, 0.5, 0.2, 0.8, 0.5, 0.5],
[0.1, 0.9, 0.4, 0.6, 0.1, 0.9], [0.7, 0.3, 0.6, 0.4, 0.0, 1.0]],
dtype=np.float64)
expected_is_benefit_z = [True, True, True, True, True, True]
np.testing.assert_allclose(obtained_z_matrix, expected_z_matrix)
self.assertEqual(obtained_z_matrix.dtype, expected_z_matrix.dtype)
self.assertEqual(obtained_is_benefit_z, expected_is_benefit_z)
def test_linear1_nested_list(self):
"""Test the Linear1 method with a nested list."""
x_matrix = [
[2.0, 12.0, 7.0, 7.0], # noqa: E201
[4.0, 100.0, 7.0, 7.0], # noqa: E201
[10.0, 200.0, 7.0, 7.0], # noqa: E201
[0.0, 300.0, 7.0, 7.0], # noqa: E201
[6.0, 400.0, 7.0, 7.0], # noqa: E201
[1.0, 600.0, 7.0, 7.0], # noqa: E201
]
is_benefit_x = [True, False, True, False]
obtained_z_matrix, obtained_is_benefit_z = mcdm.normalize(
x_matrix, is_benefit_x, "Linear1")
expected_z_matrix = np.array(
[[0.2, 1.00, 1.0, 1.0], [0.4, 0.12, 1.0, 1.0],
[1.0, 0.06, 1.0, 1.0], [0.0, 0.04, 1.0, 1.0],
[0.6, 0.03, 1.0, 1.0], [0.1, 0.02, 1.0, 1.0]],
dtype=np.float64)
expected_is_benefit_z = [True, True, True, True]
np.testing.assert_allclose(obtained_z_matrix, expected_z_matrix)
self.assertEqual(obtained_z_matrix.dtype, expected_z_matrix.dtype)
self.assertEqual(obtained_is_benefit_z, expected_is_benefit_z)