def _resample_X():
pis = model.pi(data)
X = np.array([np.random.multinomial(N_max, pis[m]) for m in xrange(M)])
N = N_vec(X).astype(np.float)
kappa = kappa_vec(X)
data["X"] = X
data["N"] = N
data["kappa"] = kappa
def resample_psi(self):
mu = self.theta_prior.mu
Lmbda = np.linalg.inv(self.theta_prior.sigma)
randvec = np.random.randn(self.D, self.T-1)
for d, c in enumerate(self.doc_topic_counts):
Lmbda_post = Lmbda + np.diag(self.omega[d])
h_post = Lmbda.dot(mu) + kappa_vec(c)
L = np.linalg.cholesky(Lmbda_post)
self.psi[d] = dpotrs(L, h_post) + solve_triangular(L, randvec[d])
