# repeat = 2
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)
# bounds = np.array([[-5, 5], [-5, 5], [-5, 5],
# [-5, 5], [-5, 5], [-5, 5]]) # for rosenbrock-6dim
repeat = 30
extreme = 1.
file_address = './solution_obj_name_' + obj_name + '_maxiter_' + str(max_iter) + \
'_repeat_' + str(repeat) + 'x'+str(extreme) + '.pkl'
if not os.path.isfile(file_address):
solution = np.empty((repeat, sig_scale.shape[0]), object)
for i in np.arange(repeat):
for j, sigma_inv in enumerate(sig_scale):
# sig_inv = np.ones(2)*sigma_inv
sig_inv = np.ones(2)*sigma_inv
sig_inv[0] = extreme
solver = EGO(sig_inv, obj, bounds, max_iter, num_ini_guess)
solution_X, solution_y = solver.solve()
solution[i, j] = (solution_X, solution_y)
# save the solution
with open(file_address, 'w') as f:
pickle.dump({'solution': solution.tolist(), 'extreme': extreme, 'obj_name': obj_name,
'max_iter': max_iter}, f)
f.close()
else:
with open(file_address, 'r') as f:
data = pickle.load(f)
f.close()
solution = np.array(data['solution'])
# sig_scale = np.array(data['sig_scale'])
max_iter = data['max_iter']
max_iter = 100
num_ini_guess = 5
bounds = np.array([[-5, 10], [0, 15]])
repeat = 30
file_address = './solution_obj_name_' + obj_name + '_maxiter_' + str(max_iter) + '_repeat_' + str(repeat) + '.pkl'
if not os.path.isfile(file_address):
solution = np.empty((repeat, sig_scale.shape[0]), object)
for i in np.arange(repeat):
for j, sigma_inv in enumerate(sig_scale):
sig_inv = np.ones(2)*sigma_inv
solver = EGO(sig_inv, obj, bounds, max_iter, num_ini_guess)
solution_X, solution_y = solver.solve()
solution[i,j] = (solution_X, solution_y)
# save the solution
with open(file_address, 'w') as f:
pickle.dump({'solution': solution.tolist(), 'sig_scale': sig_scale.tolist(), 'obj_name': obj_name,
'max_iter': max_iter}, f)
f.close()
else:
with open(file_address, 'r') as f:
data = pickle.load(f)
f.close()
solution = np.array(data['solution'])
sig_scale = np.array(data['sig_scale'])
max_iter = data['max_iter']