# This is the main Bayesian optimization loop
for i in xrange(10):
# Fit the model on the data we observed so far
model.train(X, Y)
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
plotting_range = np.linspace(X_lower[0], X_upper[0], num=1000)
ax1.plot(plotting_range, objective_function(plotting_range[:, np.newaxis]), color='b', linestyle="--")
_min_y1, _max_y1 = ax1.get_ylim()
model.visualize(ax1, X_lower[0], X_upper[0])
_min_y2, _max_y2 = ax1.get_ylim()
ax1.set_ylim(min(_min_y1, _min_y2), max(_max_y1, _max_y2))
mu, var = model.predict(new_x)
ax1.plot(new_x[0], mu[0], "r.", markeredgewidth=5.0)
ax2 = acquisition_func.plot(fig, X_lower[0], X_upper[0], plot_attr={"color": "red"}, resolution=1000)
plt.show(block=True)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
plotting_range = np.linspace(X_lower[0], X_upper[0], num=1000)
ax1.plot(plotting_range,
objective_function(plotting_range[:, np.newaxis]),
color='b',
linestyle="--")
_min_y1, _max_y1 = ax1.get_ylim()
model.visualize(ax1, X_lower[0], X_upper[0])
_min_y2, _max_y2 = ax1.get_ylim()
ax1.set_ylim(min(_min_y1, _min_y2), max(_max_y1, _max_y2))
mu, var = model.predict(new_x)
ax1.plot(new_x[0], mu[0], "r.", markeredgewidth=5.0)
ax2 = acquisition_func.plot(fig,
X_lower[0],
X_upper[0],
plot_attr={"color": "red"},
resolution=1000)
plt.show(block=True)
