def test_dcor_independence(self):
"""Test the dCor method with independent criteria."""
z_matrix = np.array([[0.0, 0.0], [0.0, 1.0]], dtype=np.float64)
obtained_corr_matrix = mcdm.correlate(z_matrix, "dCor")
expected_corr_matrix = np.array(
[[1.0000000, 0.0000000], [0.0000000, 1.0000000]], dtype=np.float64)
np.testing.assert_allclose(obtained_corr_matrix, expected_corr_matrix)
self.assertEqual(obtained_corr_matrix.dtype,
expected_corr_matrix.dtype)
def test_dcor_float32(self):
"""Test the dCor method with a float32 NumPy array."""
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.float32)
obtained_corr_matrix = mcdm.correlate(z_matrix, "dCor")
expected_corr_matrix = np.array([[1.0000000, 0.9369189, 0.9369189],
[0.9369189, 1.0000000, 1.0000000],
[0.9369189, 1.0000000, 1.0000000]],
dtype=np.float64)
np.testing.assert_allclose(obtained_corr_matrix, expected_corr_matrix)
self.assertEqual(obtained_corr_matrix.dtype,
expected_corr_matrix.dtype)
def test_abspearson_linear(self):
"""Test the AbsPearson method with a linear association."""
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)
obtained_corr_matrix = mcdm.correlate(z_matrix, "AbsPearson")
expected_corr_matrix = np.array([[1.0000000, 0.9314381, 0.9314381],
[0.9314381, 1.0000000, 1.0000000],
[0.9314381, 1.0000000, 1.0000000]],
dtype=np.float64)
np.testing.assert_allclose(obtained_corr_matrix, expected_corr_matrix)
self.assertEqual(obtained_corr_matrix.dtype,
expected_corr_matrix.dtype)
def test_pearson_nonlinear(self):
"""Test the 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_corr_matrix = mcdm.correlate(z_matrix, "Pearson")
expected_corr_matrix = np.array(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
dtype=np.float64)
np.testing.assert_allclose(obtained_corr_matrix, expected_corr_matrix)
self.assertEqual(obtained_corr_matrix.dtype,
expected_corr_matrix.dtype)
def test_pearson_float32(self):
"""Test the Pearson method with a float32 NumPy array."""
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.float32)
obtained_corr_matrix = mcdm.correlate(z_matrix, "Pearson")
expected_corr_matrix = np.array(
[
[1.0000000, 0.9314381, -0.9314381], # noqa: E201
[0.9314381, 1.0000000, -1.0000000], # noqa: E201
[-0.9314381, -1.0000000, 1.0000000]
], # noqa: E201
dtype=np.float64)
np.testing.assert_allclose(obtained_corr_matrix, expected_corr_matrix)
self.assertEqual(obtained_corr_matrix.dtype,
expected_corr_matrix.dtype)