So when I try to run the following code for arrays with more than 10k elements, it takes hours and I don’t know how to make it in the most efficient way. Any ideas?

from scipy.stats import entropy as KL import numpy as np  def dis(di,dj):     di = np.asanyarray(di)     dj = np.asanyarray(dj)     m =  0.5 * (di+dj)     kl1 = KL(di,m)     kl2 = KL(dj,m)     return 0.5*(kl1+kl2)  def Intra_Cluster_dist(C):     C = np.asanyarray(C)     K = float(C.shape[0])     factor1 = 1.0/float(K)     total_sum = 0.0     for cluster in C:         cluster = np.asanyarray(cluster)         below1 = float(cluster.shape[0])         below2 = float(below1 - 1)         sub_sum = 0.0         for di in cluster:             #others = cluster[:]              #others.remove(di)             others = cluster[np.logical_not((cluster == np.array(di)).all(axis=1))]             #for dj in others:             #    sub_sum = sub_sum +                        (2*float(dis(di,dj)))/(float(below1)*float(below2))             sub_sum = sub_sum + np.fromiter((((2*float(dis(di,dj)))/(float(below1)*float(below2))) for dj in others), dtype=float).sum()     total_sum = total_sum + sub_sum     return float(factor1 * total_sum)  def Inter_Cluster_dist(C):     K = float(len(C))     factor1 = float((1/(K*(K-1))))     total_sum = 0.0     for cluster in C:         sub_sum = 0.0         other_clusters = C[:]         other_clusters.remove(cluster)         below1= float(len(cluster))         for other in other_clusters:             below2= float(len(other))             for di in cluster:                 for dj in other:                     sub_sum = sub_sum + (float((dis(di, dj)))/float((below1*below2)))          total_sum = total_sum + sub_sum     return float(factor1 * total_sum )  def H_score(C):     return float(Intra_Cluster_dist(C))/float(Inter_Cluster_dist(C))