figure in feature space the points can be separated by plane we wish to find separating plane in using the transformed features the following python code uses the svc function of the sklearn module to solve the quadratic optimization problem ( (with the results are summarized in table the data is available from the book' github site as svmcirc csv svmquad py import numpy as np from numpy import genfromtxt from sklearn svm import svc data genfromtxt ('svmcirc csv 'delimiter =',' data [,[ , ]vectors are rows data [,[ ]reshape (len( ,labels tmp np sum(np power ( , ,axis = reshape (len( , np hstack (( ,tmp)clf svc( np inf kernel ='linear 'clf fit( ,yprint (support vectors \ "clf support_vectors_ print (support vector labels ", [clf support_ ]print ("nu",clf dual_coef_ print ("bias",clf intercept_ support vectors [ - - - - - - ]support vector labels - - nu [- - bias [ ]