def gaussian_kernel(x_1, x_2): n1 = x_1.shape[0] n2 = x_2.shape[0] result = np.zeros((n1, n2)) for i in range(n1): for j in range(n2): result[i, j] = gk.gaussian_kernel(x_1[i], x_2[j], sigma) return result
pd.plot_data(X, y) vb.visualize_boundary(clf, X, 0, 4.5, 1.5, 5) input('Program paused. Press ENTER to continue') # ===================== Part 3: Implementing Gaussian Kernel ===================== # You will now implement the Gaussian kernel to use # with the SVM. You should now complete the code in gaussianKernel.py # print('Evaluating the Gaussian Kernel') x1 = np.array([1, 2, 1]) x2 = np.array([0, 4, -1]) sigma = 2 sim = gk.gaussian_kernel(x1, x2, sigma) print( 'Gaussian kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = {} : {:0.6f}\n' '(for sigma = 2, this value should be about 0.324652'.format(sigma, sim)) input('Program paused. Press ENTER to continue') # ===================== Part 4: Visualizing Dataset 2 ===================== # The following code will load the next dataset into your environment and # plot the data # print('Loading and Visualizing Data ...') # Load from ex6data1: