def compute_reconstructions(rbm, v_data, fit, n_recon=10, vertical=False):
sampler = fit.DrivenSequentialMC(rbm)
data_state = State.from_visible(v_data, rbm)
sampler.set_positive_state(data_state)
sampler.update_positive_state(1)
v_model = rbm.deterministic_iteration(1, sampler.pos_state).units[0]
idx = numpy.random.choice(range(len(v_model)), n_recon, replace=False)
grid = numpy.array(
[[be.to_numpy_array(v_data[i]),
be.to_numpy_array(v_model[i])] for i in idx])
if vertical:
return grid
else:
return grid.swapaxes(0, 1)