def test_critic_pearson_nonlinear(self):
"""Test the CRITIC.Pearson method with a non-linear association."""
z_matrix = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.2, 0.5, 0.0],
[0.2, 0.5, 1.0], [0.4, 1.0, 0.0], [0.4, 1.0, 1.0],
[0.6, 1.0, 0.0], [0.6, 1.0, 1.0], [0.8, 0.5, 0.0],
[0.8, 0.5, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0]
],
dtype=np.float64)
obtained_w_vector = mcdm.weigh(z_matrix, "CRITIC", "Pearson")
expected_w_vector = np.array([0.27329284, 0.32664742, 0.40005975],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_vic_dcor_nested_list(self):
"""Test the VIC.dCor method with a nested list."""
z_matrix = [
[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],
]
obtained_w_vector = mcdm.weigh(z_matrix, "VIC", "dCor")
expected_w_vector = np.array([0.33817571, 0.33091215, 0.33091215],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_mw_nested_list(self):
"""Test the MW method with a nested list."""
z_matrix = [
[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],
]
obtained_w_vector = mcdm.weigh(z_matrix, "MW")
expected_w_vector = np.array([0.33333333, 0.33333333, 0.33333333],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_vic_abspearson_nested_list(self):
"""Test the VIC.AbsPearson method with a nested list."""
z_matrix = [
[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],
]
obtained_w_vector = mcdm.weigh(z_matrix, "VIC", "AbsPearson")
expected_w_vector = np.array([0.33861310, 0.33069345, 0.33069345],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_critic_pearson_nested_list(self):
"""Test the CRITIC.Pearson method with a nested list."""
z_matrix = [
[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],
]
obtained_w_vector = mcdm.weigh(z_matrix, "CRITIC", "Pearson")
expected_w_vector = np.array([0.25000000, 0.25857023, 0.49142977],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_em_nested_list(self):
"""Test the EM method with a nested list."""
z_matrix = [
[0.000, 0.000, 0.333],
[0.033, 0.050, 0.267],
[0.067, 0.100, 0.200],
[0.100, 0.175, 0.100],
[0.200, 0.200, 0.067],
[0.267, 0.225, 0.033],
[0.333, 0.250, 0.000],
]
obtained_w_vector = mcdm.weigh(z_matrix, "EM")
expected_w_vector = np.array([0.37406776, 0.25186448, 0.37406776],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)
def test_em_nonlinear(self):
"""Test the EM method with a non-linear association."""
z_matrix = np.array([[0.00000000, 0.00000000, 0.00000000],
[0.00000000, 0.00000000, 0.16666667],
[0.03333333, 0.08333333, 0.00000000],
[0.03333333, 0.08333333, 0.16666667],
[0.06666667, 0.16666667, 0.00000000],
[0.06666667, 0.16666667, 0.16666667],
[0.10000000, 0.16666667, 0.00000000],
[0.10000000, 0.16666667, 0.16666667],
[0.13333333, 0.08333333, 0.00000000],
[0.13333333, 0.08333333, 0.16666667],
[0.16666667, 0.00000000, 0.00000000],
[0.16666667, 0.00000000, 0.16666667]],
dtype=np.float64)
obtained_w_vector = mcdm.weigh(z_matrix, "EM")
expected_w_vector = np.array([0.20724531, 0.31710188, 0.47565280],
dtype=np.float64)
np.testing.assert_allclose(obtained_w_vector, expected_w_vector)
self.assertEqual(obtained_w_vector.dtype, expected_w_vector.dtype)