I'm working on adding Adjusted Mutual Information, and need to calculate the
Mutual Information.
I think I have the algorithm itself correct, except for the fact that
whenever the contingency matrix is 0, a nan happens and propogates through
the code.
Sample code on the net [1] uses an eps=np.finfo(float).eps. Should I do
this, adding eps to anything that is a denominator or parameter to log?
Is there a better way?
[1]
http://blog.sun.tc/2010/10/mutual-informationmi-and-normalized-mutual-informationnmi-for-numpy.html
FYI: My current code:
def mutual_information(labels_true, labels_pred, contingency=None):
if contingency is None:
labels_true, labels_pred = check_clusterings(labels_true,
labels_pred)
contingency = contingency_matrix(labels_true, labels_pred)
# Calculate P(i) for all i and P'(j) for all j
pi = np.sum(contingency, axis=1)
pi /= float(np.sum(pi))
pj = np.sum(contingency, axis=0)
pj /= float(np.sum(pj))
# Compute log for all values
log_pij = np.log(contingency)
# Product of pi and pj for denominator
pi_pj = np.outer(pi, pj)
# Remembering that log(x/y) = log(x) - log(y)
mi = np.sum(contingency * (log_pij - pi_pj))
return mi
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