AdjustedMutualInfoScore mishandles perfect-match limit cases

[Robertboy18]

I was comparing adjusted_mutual_info_score with scikit-learn and noticed three equivalent-partition limit cases disagree:

• empty inputs raise during expected-MI reduction
• two one-cluster labelings return 0
• two relabeled singleton partitions return 0

scikit-learn returns 1.0 because the partitions are identical. This happens with every averaging method.