DOC Fix equations for mutual_information.
@@ -538,7 +538,7 @@ def mutual_info_score(labels_true, labels_pred, contingency=None):
.. math::
- MI(U,V)=\sum_{i=1}^|U| \sum_{j=1}^|V| \\frac{|U_i\cap V_j|}{N}
+ MI(U,V)=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \\frac{|U_i\cap V_j|}{N}
\log\\frac{N|U_i \cap V_j|}{|U_i||V_j|}
This metric is independent of the absolute values of the labels:
@@ -710,7 +710,7 @@ def normalized_mutual_info_score(labels_true, labels_pred):
Normalized Mutual Information (NMI) is an normalization of the Mutual
Information (MI) score to scale the results between 0 (no mutual
information) and 1 (perfect correlation). In this function, mutual
- information is normalized by ``sqrt(H(labels_true) * H(labels_pred))``
+ information is normalized by ``sqrt(H(labels_true) * H(labels_pred))``.
This measure is not adjusted for chance. Therefore
:func:`adjusted_mustual_info_score` might be preferred.
