of the elite samples in this case we do not use any smoothing ( in the following python code the matrix sx stores the -values in the first column and the function values in the second column the rows of this matrix are sorted in ascending order according to the function valuesgiving the matrix sortsx the first elite rows of this sorted matrix correspond to the elite samples and their function values the updating of and is done in lines and figure shows how the pdfs of the (ut sampling distributions degenerate to the point mass at the global minimizer cemethod py from simann import wiggly import numpy as np np set_printoptions precision = mu sigma nnel eps *- wiggly while sigma epsx np random randn ( , )sigma np array (np ones (( , )))*mu sx np hstack ((xs( ))sortsx sx[sx [, argsort (,elite sortsx [ nel ,- mu np mean(elite axis = sigma np std(elite axis = print (' (mu){}mu{}sigma {}\nformat ( (mu)mu sigma ) (mu ) (mu ) (mu ) (mu ) (mu ) (mu ) (mu ) (mu )[ mu[ sigma [ [ mu[ sigma [ - mu[ sigma [ - mu[ sigma [ - mu[ sigma [ - mu[ sigma [ - mu[ sigma [ - - mu[ sigma [ - ( < - iteration - figure the normal pdfs of the first six sampling distributionstruncated to the interval [- the initial sampling distribution is (