gamma=gamma) print('C:', C, 'gamma:', gamma, 'weights:', w, 'bias:', b) # find number of support vectors print('# support vectors:', np.sum(alphas != 0.0)) # find overlap SV if C == 500.0 / 873: curr_sv = np.argwhere(alphas > 0) overlap_sv = np.intersect1d(prev_sv, curr_sv) print('number of overlap SV:', overlap_sv.size) prev_sv = curr_sv # calculate training and test errors err_train = SVM.SVM_kernel_test(X_train, y_train, X_train, y_train, alphas, b, gamma=gamma) err_test = SVM.SVM_kernel_test(X_test, y_test, X_train, y_train, alphas, b, gamma=gamma) print('training error:', err_train, 'test error', err_test) # Perceptron kernel print('\nPerceptron with kernel') for gamma in gammas: