distance is computed the matrix is then sorted row-wise according to these distances finallythe probabilities {pi are updated according to the mean values of the best rows the process is repeated until the {pi degenerate to binary vector this then presents the (approximatesolution clustce py import numpy as np from numpy import genfromtxt from scipy spatial distance import squareform from scipy spatial distance import pdist import matplotlib pyplot as plt def ( , ) np where ( == [ { , is the partition np where ( == [ tmp [ tmp tmp [:, return np mean(tmpthe size of the cut def maxcut ( , ,eps ,rho alpha ) shape [ ne int(rho*np / np ones(np[ while (np max(np minimum ( ,np subtract ( , ))eps) np array (np random uniform ( , ,( , )<=pdtype =np int sx np zeros (nfor in range ( )sx[is( [ ],dsortsx np flip(np argsort (sx)print (gamma ",sxsortsx [ne - ]best =",sxsortsx [ ]]elids sortsx [ neelites xelids pnew np mean(elites axis = alpha *pnew ( alpha )* return np round (pxmat genfromtxt ('clusterdata csv 'delimiter =',' xmat shape [ squareform pdist (xmat) eps *- rho alpha ce pout maxcut ( , ,eps ,rho alpha )cutval (pout ,