dlog.set_handler(['pi'], YTPlotter)
dlog.set_handler(['sigma'], YTPlotter)
dlog.set_handler('y', RFViewer, rf_shape=(D2, D2))
# Prepare ground-truth GFs (bars)
W_gt = np.zeros((H, D2, D2))
for i in xrange(D2):
W_gt[i, i, :] = bar_value
W_gt[D2 + i, :, i] = bar_value
if neg_bars > 0.0:
W_gt[sample(range(H), np.int(H * neg_bars))] *= -1
W_gt = W_gt.reshape((H, D))
W_gt += np.random.normal(size=(H, D), scale=0.5)
# Prepare model...
model = BSC_ET(D, H, Hprime, gamma, to_learn)
mparams = {'W': W_gt, 'pi': pi_gt, 'sigma': sigma_gt, 'mu': mu_gt}
mparams = comm.bcast(mparams)
pprint("Generating Model Parameters:")
pprint("pi = " + np.str(mparams['pi']) + "; sigma = " +
np.str(mparams['sigma']))
# Generate trainig data
my_N = N // comm.size
my_data = model.generate_data(mparams, my_N)
dlog.append('y', my_data['y'][0:20])
# Choose annealing schedule
anneal = LinearAnnealing(anneal_steps)
anneal['T'] = [(15, start_temp), (-10, end_temp)]
