matrix of mutual information

mat_MI.py

import numpy as np
from sklearn.metrics import mutual_info_score

def calc_MI(x, y, bins):
    c_xy = np.histogram2d(x, y, bins)[0]
    mi = mutual_info_score(None, None, contingency=c_xy)
    return mi

def calc_MI_matrix(A, bins)
    n = A.shape[1]
    matMI = np.zeros((n, n))

    for ix in np.arange(n):
        for jx in np.arange(ix+1,n):
            matMI[ix,jx] = calc_MI(A[:,ix], A[:,jx], bins)
    
    return matMI