# transform y from [0,1] to [-1,1] yy = np.ones(y.shape) yy[y == 0] = -1 # set up the SVM and learn the parameters svm = LinearSVM_twoclass() svm.theta = np.zeros((KK.shape[1], )) C = 1 svm.train(KK, yy, learning_rate=1e-4, C=C, num_iters=20000, verbose=True) # visualize the boundary utils.plot_decision_kernel_boundary(X, y, scaler, sigma, svm, '', '', ['neg', 'pos']) plt.savefig("fig4.pdf") ############################################################################ # Part 4: Training SVM with a kernel # # Select hyperparameters C and sigma # ############################################################################ # load ex4data3.mat X, y, Xval, yval = utils.loadval_mat('data/ex4data3.mat') # transform y and yval from [0,1] to [-1,1] yy = np.ones(y.shape) yy[y == 0] = -1
# transform y from [0,1] to [-1,1] yy = np.ones(y.shape) yy[y == 0] = -1 # set up the SVM and learn the parameters svm = LinearSVM_twoclass() svm.theta = np.zeros((KK.shape[1],)) C = 1 svm.train(KK,yy,learning_rate=1e-4,C=C,num_iters=20000,verbose=True) # visualize the boundary utils.plot_decision_kernel_boundary(X,y,scaler,sigma,svm,'','',['neg','pos']) plt.savefig("fig4.pdf") ############################################################################ # Part 4: Training SVM with a kernel # # Select hyperparameters C and sigma # ############################################################################ # load ex4data3.mat X,y,Xval,yval = utils.loadval_mat('data/ex4data3.mat') # transform y and yval from [0,1] to [-1,1] yy = np.ones(y.shape) yy[y == 0] = -1