def prior_pop(message):
message, z2 = prior_z2_pop(message)
message, z1 = prior_z1_pop(message)
# compute theta1
eps1_vals = codecs.std_gaussian_centres(prior_prec)[z1]
z2_vals = codecs.std_gaussian_centres(prior_prec)[z2]
theta1 = get_theta(eps1_vals, z2_vals)
return message, ((z1, z2), theta1)
def posterior_pop(message):
# pop top-down
(post_mean, post_stdd), h_rec = rec_net_top(contexts[-1])
(prior_mean, prior_stdd), h_gen = gen_net_top()
_, pop = codecs.substack(
codecs.DiagGaussian_GaussianBins(post_mean, post_stdd,
prior_mean, prior_stdd,
latent_prec, prior_prec),
z_view)
message, latent = pop(message)
latents = [(latent, (prior_mean, prior_stdd))]
for rec_net, gen_net, context in reversed(
list(zip(rec_nets, gen_nets, contexts[:-1]))):
previous_latent_val = prior_mean + \
codecs.std_gaussian_centres(prior_prec)[latents[-1][0]] * prior_stdd
(post_mean,
post_stdd), h_rec = rec_net(h_rec, previous_latent_val,
context)
(prior_mean,
prior_stdd), h_gen = gen_net(h_gen, previous_latent_val)
_, pop = codecs.substack(
codecs.DiagGaussian_GaussianBins(post_mean, post_stdd,
prior_mean, prior_stdd,
latent_prec, prior_prec),
z_view)
message, latent = pop(message)
latents.append((latent, (prior_mean, prior_stdd)))
return message, (latents[::-1], h_gen)
def posterior_append(message, latents):
# first run the model top-down to get the params and latent vals
latents, _ = latents
(post_mean, post_stdd), h_rec = rec_net_top(contexts[-1])
post_params = [(post_mean, post_stdd)]
for rec_net, latent, context in reversed(
list(zip(rec_nets, latents[1:], contexts[:-1]))):
previous_latent, (prior_mean, prior_stdd) = latent
previous_latent_val = prior_mean + \
codecs.std_gaussian_centres(prior_prec)[previous_latent] * prior_stdd
(post_mean,
post_stdd), h_rec = rec_net(h_rec, previous_latent_val,
context)
post_params.append((post_mean, post_stdd))
# now append bottom up
for latent, post_param in zip(latents, reversed(post_params)):
latent, (prior_mean, prior_stdd) = latent
post_mean, post_stdd = post_param
append, _ = codecs.substack(
codecs.DiagGaussian_GaussianBins(post_mean, post_stdd,
prior_mean, prior_stdd,
latent_prec, prior_prec),
z_view)
message = append(message, latent)
return message
def likelihood(latents):
# get the z1 vals to condition on
latents, h = latents
z1_idxs, (prior_mean, prior_stdd) = latents[0]
z1_vals = prior_mean + codecs.std_gaussian_centres(
prior_prec)[z1_idxs] * prior_stdd
return codecs.substack(obs_codec(h, z1_vals), x_view)
def posterior_pop(message):
message, z2 = post_z2_pop(message)
z2_vals = codecs.std_gaussian_centres(prior_prec)[z2]
# need to return theta1 from the z1 pop
_, post_z1_pop = codecs.substack(post1_codec(z2_vals, mu1, sig1),
z1_view)
message, (z1, theta1) = post_z1_pop(message)
return message, ((z1, z2), theta1)
def likelihood(latent):
(z1, _), theta1 = latent
# get z1_vals from the latent
_, _, mu1_prior, sig1_prior = np.moveaxis(theta1, -1, 0)
eps1_vals = codecs.std_gaussian_centres(prior_prec)[z1]
z1_vals = mu1_prior + sig1_prior * eps1_vals
append, pop = codecs.substack(obs_codec(gen_net2_partial(z1_vals)),
x_view)
return append, pop
def posterior_append(message, latents):
(z1, z2), theta1 = latents
z2_vals = codecs.std_gaussian_centres(prior_prec)[z2]
post_z1_append, _ = codecs.substack(
post1_codec(z2_vals, mu1, sig1), z1_view)
theta1[..., 0] = mu1
theta1[..., 1] = sig1
message = post_z1_append(message, z1, theta1)
message = post_z2_append(message, z2)
return message
def prior_pop(message):
# pop top-down
(prior_mean, prior_stdd), h_gen = gen_net_top()
_, pop = prior_codec
message, latent = pop(message)
latents = [(latent, (prior_mean, prior_stdd))]
for gen_net in reversed(gen_nets):
previous_latent_val = prior_mean + codecs.std_gaussian_centres(
prior_prec)[latent] * prior_stdd
(prior_mean, prior_stdd), h_gen = gen_net(h_gen,
previous_latent_val)
message, latent = pop(message)
latents.append((latent, (prior_mean, prior_stdd)))
return message, (latents[::-1], h_gen)
def likelihood(latent_idxs):
z = std_gaussian_centres(prior_prec)[latent_idxs]
return substack(obs_codec(gen_net(z)), x_view)
