return opt_update(i, gradients, state)
print('optimising the hyperparameters ...')
t0 = time.time()
for j in range(250):
opt_state = gradient_step(j, opt_state, model)
t1 = time.time()
print('optimisation time: %2.2f secs' % (t1-t0))
x_plot = np.linspace(np.min(Xall)-0.2, np.max(Xall)+0.2, 200)
# calculate posterior predictive distribution via filtering and smoothing at train & test locations:
print('calculating the posterior predictive distribution ...')
t0 = time.time()
nlpd = model.negative_log_predictive_density(t=XT, y=YT)
posterior_mean, posterior_cov = model.predict(t=x_plot)
t1 = time.time()
print('prediction time: %2.2f secs' % (t1-t0))
print('NLPD: %1.2f' % nlpd)
if save_result:
with open("output/" + str(method) + "_" + str(fold) + "_nlpd.txt", "wb") as fp:
pickle.dump(nlpd, fp)
# with open("output/" + str(method) + "_" + str(fold) + "_nlpd.txt", "rb") as fp:
# nlpd_show = pickle.load(fp)
# print(nlpd_show)
if plot_final:
link = model.likelihood.link_fn
return opt_update(i, gradients, state)
print('optimising the hyperparameters ...')
t0 = time.time()
num_iters = 500
for j in range(num_iters):
opt_state = gradient_step(j, opt_state, model)
t1 = time.time()
print('optimisation time: %2.2f secs' % (t1-t0))
# calculate posterior predictive distribution via filtering and smoothing at train & test locations:
print('calculating the posterior predictive distribution ...')
t0 = time.time()
nlpd = model.negative_log_predictive_density(t=x_test, y=y_test)
t1 = time.time()
print('prediction time: %2.2f secs' % (t1-t0))
print('test NLPD: %1.2f' % nlpd)
if save_result:
with open("output/" + str(method) + "_" + str(fold) + "_nlpd.txt", "wb") as fp:
pickle.dump(nlpd, fp)
# with open("output/" + str(method) + "_" + str(fold) + "_nlpd.txt", "rb") as fp:
# nlpd_show = pickle.load(fp)
# print(nlpd_show)
# lb = posterior_mean[:, 0] - 1.96 * posterior_var[:, 0]**0.5
# ub = posterior_mean[:, 0] + 1.96 * posterior_var[:, 0]**0.5
# x_pred = model.t_all[:, 0]
