def test_saw_float32(self):
"""Test the SAW method with float32 NumPy arrays."""
z_matrix = np.array([[0.00, 1.00], [0.25, 0.75], [0.50, 0.50],
[0.75, 0.25], [1.00, 0.00]],
dtype=np.float32)
is_benefit_z = [True, True]
w_vector = np.array([0.5, 0.5], dtype=np.float32)
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "SAW")
expected_s_vector = np.array([0.5, 0.5, 0.5, 0.5, 0.5],
dtype=np.float64)
expected_desc_order = True
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
def test_saw_simple_cost(self):
"""Test the SAW method with simple cost criteria."""
z_matrix = np.array(
[[0.5, 0.6, 0.3, 0.2, 0.9], [0.5, 0.5, 0.5, 0.5, 0.5],
[0.5, 0.4, 0.7, 0.8, 0.1]],
dtype=np.float64)
is_benefit_z = [False, False, False, False, False]
w_vector = np.array([0.0, 0.1, 0.2, 0.3, 0.4], dtype=np.float64)
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "SAW")
expected_s_vector = np.array([0.54, 0.5, 0.46], dtype=np.float64)
expected_desc_order = False
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
def test_mtopsis_balanced(self):
"""Test the mTOPSIS method with a balanced decision matrix."""
z_matrix = np.array([[0.00, 1.00], [0.25, 0.75], [0.50, 0.50],
[0.75, 0.25], [1.00, 0.00]],
dtype=np.float64)
is_benefit_z = [True, True]
w_vector = np.array([0.5, 0.5], dtype=np.float64)
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "mTOPSIS")
expected_s_vector = np.array([0.5, 0.5, 0.5, 0.5, 0.5],
dtype=np.float64)
expected_desc_order = True
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
def test_mtopsis_simple_mixture(self):
"""Test the mTOPSIS method with a mixture of criteria."""
z_matrix = np.array(
[[0.5, 0.6, 0.3, 0.2, 0.9], [0.5, 0.5, 0.5, 0.5, 0.5],
[0.5, 0.4, 0.7, 0.8, 0.1]],
dtype=np.float64)
is_benefit_z = [True, False, True, True, True]
w_vector = np.array([0.0, 0.1, 0.2, 0.3, 0.4], dtype=np.float64)
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "mTOPSIS")
expected_s_vector = np.array([0.5714286, 0.5000000, 0.4285714],
dtype=np.float64)
expected_desc_order = True
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
def test_topsis_simple_benefit(self):
"""Test the TOPSIS method with simple benefit criteria."""
z_matrix = np.array(
[[0.5, 0.6, 0.3, 0.2, 0.9], [0.5, 0.5, 0.5, 0.5, 0.5],
[0.5, 0.4, 0.7, 0.8, 0.1]],
dtype=np.float64)
is_benefit_z = [True, True, True, True, True]
w_vector = np.array([0.0, 0.1, 0.2, 0.3, 0.4], dtype=np.float64)
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "TOPSIS")
expected_s_vector = np.array([0.6194425, 0.5000000, 0.3805575],
dtype=np.float64)
expected_desc_order = True
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
def test_topsis_nested_list(self):
"""Test the TOPSIS method with nested lists."""
z_matrix = [
[0.00, 1.00],
[0.25, 0.75],
[0.50, 0.50],
[0.75, 0.25],
[1.00, 0.00],
]
is_benefit_z = [True, True]
w_vector = [0.5, 0.5]
obtained_s_vector, obtained_desc_order = mcdm.score(
z_matrix, is_benefit_z, w_vector, "TOPSIS")
expected_s_vector = np.array([0.5, 0.5, 0.5, 0.5, 0.5],
dtype=np.float64)
expected_desc_order = True
np.testing.assert_allclose(obtained_s_vector, expected_s_vector)
self.assertEqual(obtained_s_vector.dtype, expected_s_vector.dtype)
self.assertEqual(obtained_desc_order, expected_desc_order)
