normalise.py

def normalise(mat, axis):
    if axis == 0:
        row_sums = mat.sum(axis=1)
        new_matrix = mat / row_sums[:, np.newaxis]
        return new_matrix
    if axis == 1:
        col_sums = mat.sum(axis=0)
        new_matrix = mat / col_sums[np.newaxis, :]
        return new_matrix
      

L0 = normalise(sat, 0)
start_acc = comm_acc(S0, L0)

S, L = S0, L0
for i in range(100):
    S = normalise(L, 1)
    L = normalise(S, 0)