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:
