def plot_svm():
num_points = 10
(X, y, f) = random_linearly_separable_data(num_points, bounds)
(w_svm, vectors) = linsep_svm(X, y)
x_plot = X[:,0]
y_plot = X[:,1]
s = 20*np.ones(num_points)
s[vectors] = 60
pla = PLA(bounds=bounds)
pla.fit(X, y)
w_pla = pla.weights
# test values for b
print("Average b:", w_svm[0])
each_b = y[vectors] - np.dot(X[vectors], w_svm[1:])
print("Individual b's:", each_b)
(x_f, y_f) = weights_to_mxb_2D(f, bounds)
(x_w, y_w) = weights_to_mxb_2D(w_svm, bounds)
(x_p, y_p) = weights_to_mxb_2D(w_pla, bounds)
c = np.where(y==1, 'r', 'b')
plt.scatter(x_plot,y_plot, s, c=c)
plt.plot(x_f, y_f, 'k-.')
plt.plot(x_w, y_w, 'b')
plt.plot(x_p, y_p, 'r--')
plt.xlim([-1, 1])
plt.ylim([-1, 1])
plt.grid()
plt.show()
def answers():
num_points = 10
num_experiments = 1000
times_svm_better = 0.0
total_num_vectors = 0.0
for i in range(num_experiments):
(X, y, f) = random_linearly_separable_data(num_points, bounds)
(w_svm, vectors) = linsep_svm(X, y)
pla = PLA(bounds=bounds)
pla.fit(X, y)
w_pla = pla.weights
E_svm = linear_randomized_Eout(f, w_svm, bounds)
E_pla = linear_randomized_Eout(f, w_pla, bounds)
if E_svm < E_pla:
times_svm_better += 1
total_num_vectors += len(vectors)
print("Number of points used: ", num_points)
print("Proportion of times SVM beats PLA:", times_svm_better/num_experiments)
print("Average number of support vectors:", total_num_vectors/num_experiments)