def step(self):
# S ~ N(0, exp(Z / 2))
#
# Z = signal_node + noise_node + bias
# = signal_node + gaussian_term
# = scale_node + bias
#
# resample gaussian_term conditioned on signal_node
scale_node = self.gsm_node.scale_node
S = self.gsm_node.value()
N, K = S.shape
# resample Z
Z = scale_node.value() + self.gsm_node.bias
if scale_node.isleaf():
mu = self.gsm_node.bias * np.ones((N, K))
sigma_sq = scale_node.variance()
else:
assert scale_node.issum()
mu = self.gsm_node.bias + scale_node.value() - scale_node.children[-1].value()
sigma_sq = scale_node.children[-1].variance()
for i in range(N):
for k in range(K):
log_f = sparse_coding.LogFUncollapsed(S[i, k])
if self.maximize:
temp = lambda z: -log_f(z) - distributions.gauss_loglik(z, mu[i, k], sigma_sq[i, k])
Z[i, k] = scipy.optimize.fmin(temp, Z[i, k], disp=False)
else:
Z[i, k] = slice_sampling.slice_sample_gauss(log_f, mu[i, k], sigma_sq[i, k], Z[i, k])
# resample bias
if scale_node.isleaf():
gaussian_term = Z
else:
signal = scale_node.value() - scale_node.children[-1].value()
gaussian_term = Z - signal
if not self.maximize:
if self.gsm_node.bias_type == 'scalar':
mu = gaussian_term.mean()
lam = (1. / sigma_sq).sum()
self.gsm_node.bias = np.random.normal(mu, 1. / lam)
elif self.gsm_node.bias_type == 'row':
mu = gaussian_term.mean(1)
lam = (1. / sigma_sq).sum(1)
self.gsm_node.bias = np.random.normal(mu, 1. / lam)[:, nax]
elif self.gsm_node.bias_type == 'col':
mu = gaussian_term.mean(0)
lam = (1. / sigma_sq).sum(0)
self.gsm_node.bias = np.random.normal(mu, 1. / lam)[nax, :]
# set noise node
noise_term = gaussian_term - self.gsm_node.bias
if scale_node.isleaf():
scale_node.set_value(noise_term)
else:
scale_node.children[-1].set_value(noise_term)
def sample_Z(state):
N, K= state.S.shape[0], state.Z.shape[1]
for i in range(N):
for k in range(K):
log_f = LogFUncollapsed(state.S[i, k])
if np.isscalar(state.mu_Z):
mu_Z = state.mu_Z
else:
mu_Z = state.mu_Z[k]
state.Z[i, k] = slice_sampling.slice_sample_gauss(log_f, mu_Z, state.sigma_sq_Z, state.Z[i, k])
if hasattr(debugger, 'after_sample_Z'):
debugger.after_sample_Z(vars())
