def test_12_34_and_zeros(self):
"""Compute mi for identical arrays [[1, 2], [2, 4]]."""
self.y_dataframe = InferelatorData(expression_data=np.zeros((2, 2)))
self.clr_matrix, self.mi_matrix = mi.context_likelihood_mi(
self.x_dataframe, self.y_dataframe)
# the entire clr matrix is NAN
self.assertTrue(np.isnan(self.clr_matrix.values).all())
def test_12_34_swapped(self):
"""Compute mi for identical arrays [[1, 2], [2, 4]]."""
self.y_dataframe = L2.copy()
self.clr_matrix, self.mi_matrix = mi.context_likelihood_mi(
self.x_dataframe, self.y_dataframe)
expected = np.array([[0, 1], [1, 0]])
np.testing.assert_almost_equal(self.clr_matrix.values, expected)
def test_12_34_transposed(self):
"Compute mi for identical arrays [[1, 2], [2, 4]]."
self.y_dataframe = InferelatorData(
expression_data=np.array([[1, 2], [3, 4]]))
self.clr_matrix, self.mi_matrix = mi.context_likelihood_mi(
self.x_dataframe, self.y_dataframe)
expected = np.array([[0, 1], [1, 0]])
np.testing.assert_almost_equal(self.clr_matrix.values, expected)