init_data.append_sample_points([
SamplePoint(pt, [init_pts_value[num, i]
for i in observations], objective_func._sample_var)
for num, pt in enumerate(init_pts)
])
cpp_cov = cppSquareExponential(np.ones(objective_func._dim + 1))
noise_variance = np.ones(1 + len(derivatives))
hyper_domain = np.zeros((2 + dim + len(derivatives), 2))
hyper_domain[:(2 + dim), :] = objective_func._hyper_domain[:(2 + dim), :]
for g in range(len(derivatives)):
hyper_domain[g + 2 +
dim, :] = objective_func._hyper_domain[derivatives[g] + 2 +
dim, :]
hyper_search_domain = cppTensorProductDomain(
[ClosedInterval(bound[0], bound[1]) for bound in hyper_domain])
hyper_params = np.ones(2 + dim + len(derivatives))
cpp_gp = cppGaussianProcess(cpp_cov, hyper_params[(1 + objective_func._dim):],
init_data, derivatives)
cpp_gp_loglikelihood = cppGaussianProcessLogLikelihood(
cpp_cov, cpp_gp.get_historical_data_copy(), noise_variance, derivatives)
sgd_optimizer = cppGradientDescentOptimizer(hyper_search_domain,
cpp_gp_loglikelihood,
cpp_sgd_params_hyper)
hyper_params = multistart_hyperparameter_optimization(
log_likelihood_optimizer=sgd_optimizer,
num_multistarts=None,
max_num_threads=8)
objective_func = obj_func_dict[obj_func_name]
dim = int(objective_func._dim)
num_initial_points = initial_n
num_fidelity = objective_func._num_fidelity
inner_search_domain = pythonTensorProductDomain(
[
ClosedInterval(
objective_func._search_domain[i, 0], objective_func._search_domain[i, 1]
)
for i in range(objective_func._search_domain.shape[0] - num_fidelity)
]
)
cpp_search_domain = cppTensorProductDomain(
[ClosedInterval(bound[0], bound[1]) for bound in objective_func._search_domain]
)
cpp_inner_search_domain = cppTensorProductDomain(
[
ClosedInterval(
objective_func._search_domain[i, 0], objective_func._search_domain[i, 1]
)
for i in range(objective_func._search_domain.shape[0] - num_fidelity)
]
)
# get the initial data
init_pts = np.zeros((initial_n, objective_func._dim))
init_pts[
:, : objective_func._dim - objective_func._num_fidelity
] = inner_search_domain.generate_uniform_random_points_in_domain(initial_n)
objective_func = synthetic_functions.RandomGP(
gp_grad_info_dict['dim'], gp_grad_info_dict['hyper_params'],
hist_data_grad)
hyper_params = gp_grad_info_dict['hyper_params']
init_pts = [[-2.0], [0.0], [0.3], [0.5]]
ymax = 1
else:
objective_func = obj_func_dict[obj_func_name]
#init_data = utils.get_init_data_from_db(objective_func._dim, objective_func._sample_var, utils.sql_engine, 'init_points_'+obj_func_name)
python_search_domain = pythonTensorProductDomain([
ClosedInterval(bound[0], bound[1])
for bound in objective_func._search_domain
])
cpp_search_domain = cppTensorProductDomain([
ClosedInterval(bound[0], bound[1])
for bound in objective_func._search_domain
])
result = numpy.zeros((num_iteration, 6))
best_so_far_kg = numpy.zeros((end_idx - start_idx, num_iteration + 1))
# begin job
for job_no in xrange(start_idx, end_idx):
python_search_domain = pythonTensorProductDomain([
ClosedInterval(bound[0], bound[1])
for bound in objective_func._search_domain
])
init_value = [objective_func.evaluate(pt) for pt in init_pts]
init_data = HistoricalData(objective_func._dim, 1)
