def onclick(event):
plt.close('all')
point_x, point_y = event.xdata, event.ydata
data_points.append(point_x)
data_targets.append(point_y)
x_tr = np.array(data_points).reshape(-1)[None, :]
y_tr = np.array(data_targets)
new_gp = GPR(model_covariance_obj, method=method)
# new_gp.fit(x_tr, y_tr, max_iter=max_iter)
print(new_gp.covariance_obj.get_params())
predicted_y_test, high, low = new_gp.predict(x_test, x_tr, y_tr)
fig = plt.figure()
gp_plot_reg_data(x_tr, y_tr, 'yo')
gp_plot_reg_data(x_test, predicted_y_test, 'b')
gp_plot_reg_data(x_test, means_y_test, '--b')
gp_plot_reg_data(means_inducing_points, means_mean, 'bo', markersize=12)
gp_plot_reg_data(x_test, means_low, '--g')
gp_plot_reg_data(x_test, means_high, '--r')
gp_plot_reg_data(x_test, low, 'g-')
gp_plot_reg_data(x_test, high, 'r-')
gp_plot_reg_data(x_test, y_test, 'y-')
fig.canvas.mpl_connect('button_press_event', onclick)
plt.ylim(-2, 2)
plt.xlim(0, 1)
plt.show()
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 1:
x_test = np.linspace(0, 1, test_num)
x_test = x_test.reshape(1, test_num)
else:
x_test = np.random.rand(dim, test_num)
y_tr, y_test = gp.generate_data(x_tr, x_test, seed=seed)
data_points = []
data_targets = []
fig = plt.figure()
gp_plot_reg_data(x_test, y_test, 'y-')
means_gp = GPR(model_covariance_obj, method='means')
means_gp.fit(x_tr, y_tr, num_inputs=ind_inputs_num, optimizer_options=lbfgsb_options)
print(model_covariance_obj.get_params())
means_inducing_points, means_mean, means_cov = means_gp.inducing_inputs
means_y_test, means_high, means_low = means_gp.predict(x_test)
def onclick(event):
plt.close('all')
point_x, point_y = event.xdata, event.ydata
data_points.append(point_x)
data_targets.append(point_y)
x_tr = np.array(data_points).reshape(-1)[None, :]