It may seem a silly question that, how to compare two matrices with the same dimension by Hamming distance?
$$d^{HD}(i,j) = \sum_{k=0}^{n-1} [y_{ik}\neq y_{jk} ]$$
- Is it Simply
sum(abs(B-A))whereAandBare the matrices? - Is it used for weighted matrices or just used for binary ones?
In some packages like Scipy, pdist(X,"hamming")
Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors u and v which disagree. To save memory, the matrix X can be of type boolean.
The output is a matrix n by n(squareform).
I don't know how to apply for matrices?