def plot_gaussian_process_prediction(figure_handle, mu, test_data, mean_est, mu_test_fct, title):
ax = Axes3D(figure_handle)
# Make the panes transparent
ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
# Make the grid lines transparent
ax.xaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.yaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.zaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
# Remove axis
ax._axis3don = False
# Initial view
# ax.view_init(elev=10, azim=-20.) # (default: elev=30, azim=-60)
ax.view_init(elev=10, azim=30.) # (default: elev=30, azim=-60)
# Plot sphere
plot_sphere(ax, alpha=0.4)
# Plot training data on the manifold
plt_scale_fact = test_function(mu_test_fct, mu_test_fct)[0, 0]
nb_data_test = test_data.shape[0]
for n in range(nb_data_test):
ax.scatter(test_data[n, 0], test_data[n, 1], test_data[n, 2],
c=[pl.cm.inferno(mean_est[n] / plt_scale_fact)])
# Plot mean of Gaussian test function
ax.scatter(mu[0], mu[1], mu[2], c='g', marker='D')
plt.title(title, size=25)
ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
# Make the grid lines transparent
ax.xaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.yaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.zaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
# Remove axis
ax._axis3don = False
# Initial view
# ax.view_init(elev=10, azim=-20.) # (default: elev=30, azim=-60)
ax.view_init(elev=10, azim=30.) # (default: elev=30, azim=-60)
# Plot sphere
plot_sphere(ax, alpha=0.4)
# Plot evaluated points
if max_colors is None:
max_colors = np.max(y_eval - true_opt_val[0])
for n in range(x_eval.shape[0]):
ax.scatter(x_eval[n, 0],
x_eval[n, 1],
x_eval[n, 2],
c=pl.cm.inferno(1. -
(y_eval[n] - true_opt_val[0]) / max_colors))
# Plot true minimum
ax.scatter(true_min[0, 0],
true_min[0, 1],
true_min[0, 2],