# Obtain next sampling point from the acquisition function (expected_improvement)
X_next = propose_location(expected_improvement, X_sample, Y_sample,
gpr, bounds)
# Obtain next noisy sample from the objective function
Y_next = f_x(X_next, noise)
# Plot samples, surrogate function, noise-free objective and next sampling location
plt.subplot(n_iter, 2, 2 * i + 1)
plot_approximation(gpr,
X,
Y,
X_sample,
Y_sample,
X_next,
show_legend=i == 0)
plt.title(f'Iteration {i + 1}')
plt.subplot(n_iter, 2, 2 * i + 2)
plot_acquisition(X,
expected_improvement(X, X_sample, Y_sample, gpr),
X_next,
show_legend=i == 0)
# Add sample to previous samples
X_sample = np.vstack((X_sample, X_next))
Y_sample = np.vstack((Y_sample, Y_next))
plot_convergence(X_sample, Y_sample)
for fun_idx, f in enumerate(f_list):
Y_next_list.append(f(X_next, noise=noise))
# Plot samples, surrogate function, noise-free objective and next sampling location
plt.subplot(n_iter, 2, 2 * i + 1)
plot_approximation(gpr,
X,
Y_alpha,
X_sample,
Y_sample_alpha,
X_next,
show_legend=i == 0)
plt.title(f'Iteration {i + 1}')
plt.subplot(n_iter, 2, 2 * i + 2)
plot_acquisition(X,
expected_improvement(X, X_sample, Y_sample_alpha,
gpr),
X_next,
show_legend=i == 0)
# Add sample to previous samples
X_sample = np.vstack((X_sample, X_next))
for fun_idx, Y_sample in enumerate(Y_sample_list):
Y_sample_list[fun_idx] = np.vstack(
[Y_sample_list[fun_idx], Y_next_list[fun_idx]])
Y_sample_alpha = Y_sample_list[0] + alpha * Y_sample_list[1]
plot_convergence(X_sample, Y_sample_alpha)