def sample_theta(self):
self.theta_hist.append(deepcopy(self.theta))
for label in xrange(self.n_labels):
dirichlet_params = util.v_sum([self.gamma_theta] * self.N, self.c_word_label[label])
self.theta[label] = util.sample_dirichlet(dirichlet_params)
if self.debug:
print 'Dir-params:', dirichlet_params
print 'theta:', self.theta[label]
def compute_average_theta(self,n_iter=None):
self.avg_theta = [None] * self.n_labels
norm = 0
for theta in self.theta_hist[self.burn_in:n_iter:self.lag]:
norm += 1
for topic, probs in enumerate(theta):
if topic not in self.avg_theta:
self.avg_theta[topic] = probs
else:
self.avg_theta[topic] = util.v_sum(self.avg_theta[topic], probs)
for probs in self.avg_theta:
#self.avg_theta[topic] = [x/float(norm) for x in self.avg_theta[topic]]
util.normalize(probs)
