BayesianOptimizer_optimize.py

  def _extend_prior_with_posterior_data(self, x,y):
        self.x_init = np.append(self.x_init, np.array([x]), axis = 0)
        self.y_init = np.append(self.y_init, np.array(y), axis = 0)
  
  def optimize(self):
        y_max_ind = np.argmax(self.y_init)
        y_max = self.y_init[y_max_ind]
        optimal_x = self.x_init[y_max_ind]
        optimal_ei = None
        for i in range(self.n_iter):
            self.gauss_pr.fit(self.x_init, self.y_init)
            x_next, ei = self._get_next_probable_point()
            y_next = self.target_func(np.array([x_next]))
            self._extend_prior_with_posterior_data(x_next,y_next)
            
            if y_next[0] > y_max:
                y_max = y_next[0]
                optimal_x = x_next
                optimal_ei = ei[0]

            if i == 0:
                 prev_x = x_next
            else:
                self.distances_.append(np.linalg.norm(prev_x - x_next))
                prev_x = x_next
            
            self.best_samples_ = self.best_samples_.append({"y": y_max, "ei": optimal_ei},ignore_index=True)
        
        return optimal_x, y_max