)
test = np.zeros(eval_pts.shape[0])
ps = PosteriorMeanMCMC(cpp_gp_loglikelihood.models, num_fidelity)
for i, pt in enumerate(eval_pts):
ps.set_current_point(
pt.reshape((1, cpp_gp_loglikelihood.dim - objective_func._num_fidelity))
)
test[i] = -ps.compute_objective_function()
report_point = eval_pts[np.argmin(test)].reshape(
(1, cpp_gp_loglikelihood.dim - objective_func._num_fidelity)
)
py_repeated_search_domain = RepeatedDomain(num_repeats=1, domain=inner_search_domain)
ps_mean_opt = pyGradientDescentOptimizer(
py_repeated_search_domain, ps, py_sgd_params_ps
)
report_point = multistart_optimize(ps_mean_opt, report_point, num_multistarts=1)[0]
report_point = report_point.ravel()
report_point = np.concatenate((report_point, np.ones(objective_func._num_fidelity)))
print(
"best so far in the initial data {0}".format(
true_value_init[np.argmin(true_value_init[:, 0])][0]
)
)
capital_so_far = 0.0
start = time.time()
for n in range(num_iteration):
print(
method
(1,
cpp_gp_loglikelihood.dim - objective_func._num_fidelity)))
test[i] = -pvar.compute_objective_function()
initial_points = np.zeros(
(20, cpp_gp_loglikelihood.dim - objective_func._num_fidelity))
indices = np.argsort(test)
for i in range(20):
initial_points[i, :] = eval_pts[indices[i]]
#initial_point = eval_pts[np.argmin(test)].reshape((1, cpp_gp_loglikelihood.dim-objective_func._num_fidelity))
py_repeated_search_domain = RepeatedDomain(num_repeats=1,
domain=inner_search_domain)
pvar_mean_opt = pyGradientDescentOptimizer(py_repeated_search_domain,
pvar,
py_sgd_params_acquisition)
report_point = multistart_optimize(pvar_mean_opt,
initial_points,
num_multistarts=20)[0]
pvar.set_current_point(
report_point.reshape(
(1, cpp_gp_loglikelihood.dim - objective_func._num_fidelity)))
if -pvar.compute_objective_function() > np.min(test):
report_point = initial_points[[0]]
next_points = report_point
voi = np.nan
elif method == 'PI':
eval_pts = inner_search_domain.generate_uniform_random_points_in_domain(
int(1e3))
