def one_iter(self):
k = C(1.0) * CustomRBF(1.0) + WhiteKernel(1.0)
gp = GaussianProcessRegressor(kernel=k, alpha=0.01, normalize_y=True, n_restarts_optimizer=10)
gp.custom_fit(self.DXX, self.Y)
mu, std = gp.custom_predict(self.DZZ, self.DXZ, return_std=True)
# record the best kernel ever seen
min_value = np.min(self.Y)
best_ker = self.observations[np.argmin(self.Y)]
self.record_best_ker.append(best_ker)
# remember to reshape mu and std
ei = self.EI(mu.reshape(-1), min_value, std.reshape(-1), 'min')
next_i = np.argmax(ei)
# update candidate
if self.bayesian is None:
self.update_candidates_nonbayesian(next_i)
else:
self.update_candidates_bayesian(next_i)
return min_value
