def collapsed_gibbs(self, wordIds, S, T):
K = self.K
V = self.V
D = self.D
N = self.N
latent_vars = {}
training_data = {}
qbeta = Empirical(tf.Variable(tf.zeros([S, K, V]) + 0.01))
latent_vars[self.beta] = qbeta
qtheta = [None] * D
qz = [None] * D
for d in range(D):
qtheta[d] = Empirical(tf.Variable(tf.zeros([S, K]) + 0.1))
latent_vars[self.theta[d]] = qtheta[d]
qz[d] = Empirical(tf.Variable(tf.zeros([S, N[d]], dtype=tf.int32)))
latent_vars[self.z[d]] = qz[d]
training_data[self.w[d]] = wordIds[d]
self.latent_vars = latent_vars
proposal_vars = {}
proposal_vars[self.beta] = ed.complete_conditional(self.beta)
cond_set = set(self.w + self.z)
for d in range(D):
proposal_vars[self.theta[d]] = \
ed.complete_conditional(self.theta[d])
proposal_vars[self.z[d]] = \
ed.complete_conditional(self.z[d], cond_set)
self.inference = ed.Gibbs(latent_vars, proposal_vars, training_data)
print("collapsed gibbs setup finished")
self.inference.initialize(n_iter=T, n_print=1)
print("initialize finished")
self.__run_inference__(T)
self.qbeta_sample = qbeta.eval()
def gibbs(self, wordIds, S, T):
K = self.K
V = self.V
D = self.D
N = self.N
latent_vars = {}
training_data = {}
qbeta = Empirical(tf.Variable(tf.zeros([S, K, V]) + 0.01))
latent_vars[self.beta] = qbeta
qtheta = [None] * D
qz = [None] * D
for d in range(D):
qtheta[d] = Empirical(tf.Variable(tf.zeros([S, K]) + 0.1))
latent_vars[self.theta[d]] = qtheta[d]
qz[d] = Empirical(tf.Variable(tf.zeros([S, N[d]], dtype=tf.int32)))
latent_vars[self.z[d]] = qz[d]
training_data[self.w[d]] = wordIds[d]
self.latent_vars = latent_vars
self.inference = ed.Gibbs(latent_vars, data=training_data)
print("gibbs setup finished")
self.inference.initialize(n_iter=T, n_print=1)
self.__run_inference__(T)
self.qbeta_sample = qbeta.eval()
