)
eval_pts = np.reshape(
np.append(
eval_pts,
(cpp_gp.get_historical_data_copy()).points_sampled[
:, : (cpp_gp_loglikelihood.dim - objective_func._num_fidelity)
],
),
(
eval_pts.shape[0] + cpp_gp.num_sampled,
cpp_gp.dim - objective_func._num_fidelity,
),
)
test = np.zeros(eval_pts.shape[0])
ps_evaluator = PosteriorMean(cpp_gp, num_fidelity)
for i, pt in enumerate(eval_pts):
ps_evaluator.set_current_point(
pt.reshape(
(1, cpp_gp_loglikelihood.dim - objective_func._num_fidelity)
)
)
test[i] = -ps_evaluator.compute_objective_function()
initial_point = eval_pts[np.argmin(test)]
ps_sgd_optimizer = cppGradientDescentOptimizer(
cpp_inner_search_domain, ps_evaluator, cpp_sgd_params_ps
)
report_point = posterior_mean_optimization(
ps_sgd_optimizer, initial_guess=initial_point, max_num_threads=4
plt.plot(discrete_pts.flatten(), temp_mean, 'k')
plt.fill_between(discrete_pts.flatten(),
temp_mean - temp_std,
temp_mean + temp_std,
color='b',
alpha=0.2)
x_axis = cpp_gp_nogradient.get_historical_data_copy(
).points_sampled
y_axis = cpp_gp_nogradient.get_historical_data_copy(
).points_sampled_value[:, 0]
plt.plot(x_axis, y_axis, 'bs')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
ps_evaluator = PosteriorMean(cpp_gp_nogradient, 0)
ps_sgd_optimizer = cppGradientDescentOptimizer(
cpp_search_domain, ps_evaluator, cpp_sgd_params_ps)
fig, ax = plt.subplots(figsize=(figwidth, figheight))
ax.set_ylim(0, 0.5)
Y = None
#for num, mc in enumerate([20, 100, 1000]):
cpp_kg_evaluator_nogradient = cppKnowledgeGradient(
gaussian_process=cpp_gp_nogradient,
num_fidelity=0,
inner_optimizer=ps_sgd_optimizer,
discrete_pts=init_points,
num_mc_iterations=100)
xlist = discrete_pts.flatten()
