on which in turn gives rise to (uniquerkhs the optimal prediction function ( is now of the form gt (xa where and yi li ph(xi )ph(xb bph( ) = ( byi li ph(xi = the decision boundary{ gt ( }is again circle in the following code determines the fitted model parameters and the decision boundary figure shows the optimal decision boundarywhich is identical to ( the function mykernel specifies the custom kernel above svmkern py import numpy as np matplotlib pyplot as plt from numpy import genfromtxt from sklearn svm import svc def mykernel ( , )tmpu np sum(np power ( , ,axis = reshape (len( , np hstack (( ,tmpu)tmpv np sum(np power ( , ,axis = reshape (len( , np hstack (( ,tmpv) print ( shape return read in the data inp genfromtxt ('svmcirc csv 'delimiter =','data inp [,[ , ]vectors are rows inp [,[ ]reshape (len(data,labels clf svc( np inf kernel =mykernel gamma ='auto 'custom kernel clf svc( np inf kernel =rbf"gamma =scale 'inbuilt clf fit(data ,yprint (support vectors \ "clf support_vectors_ print (support vector labels ", [clf support_ ]print ("nu ",clf dual_coef_ print ("bias ",clf intercept_ plot x_min x_max - , y_min y_max - , xx yy np meshgrid (np arange (x_min x_max )np arange (y_min y_max )plt plot(data[clf support_ , data[clf support_ , ,'go'plt plot(data[ == , data[ == , ' 'plt plot(data[ =- , data[ =- , ,'rx '