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()
