for n, k in enumerate(obj_names): file_address = './IRL_solution_obj_name_' + obj_names[n] + '_maxiter_' + str(max_iter)\ + '_repeat_' + str(repeat) + '.pkl' if not os.path.isfile(file_address): solution = np.empty((repeat, sig_scale.shape[0]), object) # strategy_log = [] for i in np.arange(repeat): for j, sigma_inv in enumerate(sig_scale): sig_inv = np.ones(boundss[n].shape[0])*sigma_inv solver = EGO(sig_inv, objs[n], boundss[n], max_iter, num_ini_guess) solver.irl_strat("rosen30_IRL_strat.csv") # only for IRL ego solution_X, solution_y = solver.solve() # strategy_log += [solver.concentrated_likelihood_history] # only for CLO ego solution[i, j] = (solution_X, solution_y) # print strategy_log # save the solution with open(file_address, 'w') as f: pickle.dump({'solution': solution.tolist(), 'sig_scale': sig_scale.tolist(), 'obj_name': obj_names[n], 'max_iter': max_iter}, f) f.close() # np.savetxt('CLO_strategy_log.txt', np.array(strategy_log)) else: with open(file_address, 'r') as f: data = pickle.load(f) f.close()