def linsep_logistic(num_points=100, num_experiments=100, tol=0.01, eta=0.01, max_iter=1000):
all_epochs = np.zeros(num_experiments)
all_E_out = np.zeros(num_experiments)
for i in range(num_experiments):
(X, y, f) = random_linearly_separable_data(num_points, bounds)
(weights, num_epochs) = logistic_gradient_descent(X, y, tol, eta, max_iter)
E_out = cross_entropy_randomized_Eout(f, weights, bounds)
all_epochs[i] = num_epochs
all_E_out[i] = E_out
print(i+1, num_epochs, E_out)
return all_epochs.mean(), all_E_out.mean()
def test(num_points=100, tol=0.01, eta=0.01, max_iter=2000):
(X, y, f) = random_linearly_separable_data(num_points, bounds)
(w, num) = logistic_gradient_descent(X, y, tol, eta, max_iter)
print(num, cross_entropy_randomized_Eout(f, w, bounds))
positives = X[np.where(y==1)]
negatives = X[np.where(y==-1)]
x_p = positives[:,0]
y_p = positives[:,1]
x_n = negatives[:,0]
y_n = negatives[:,1]
(w_x, w_y) = (bounds[:2], -np.array(bounds[:2])*w[1]/w[2] - w[0]/w[2])
(f_x, f_y) = (bounds[:2], -np.array(bounds[:2])*f[1]/f[2] - f[0]/f[2])
plt.plot(w_x, w_y, c='b')
plt.plot(f_x, f_y, c='k')
plt.scatter(x_p, y_p, c='b', marker='o')
plt.scatter(x_n, y_n, c='r', marker='o')
plt.xlim(bounds[:2])
plt.ylim(bounds[2:4])
plt.show()
