def __init__(self, data, T, alpha_beta):
mu, sigma = compute_uniform_mean_psi(T)
self.theta_prior = Gaussian(
mu=mu, sigma=sigma, mu_0=mu, sigma_0=T*sigma/10.,
nu_0=T/10., kappa_0=1./10)
self.ppgs = initialize_polya_gamma_samplers()
self.omega = np.zeros((data.shape[0], T-1))
super(StickbreakingCorrelatedLDA, self).__init__(data, T, alpha_beta)
def __init__(self, data, T, alpha_beta):
mu, sigma = np.zeros(T), np.eye(T)
self.theta_prior = \
Gaussian(
mu=mu, sigma=sigma, mu_0=mu, sigma_0=T*sigma/10.,
nu_0=T/10., kappa_0=10.)
self.ppgs = initialize_polya_gamma_samplers()
self.omega = np.zeros((data.shape[0], T))
super(LogisticNormalCorrelatedLDA, self).__init__(data, T, alpha_beta)
def monte_carlo_approx(M=100000):
ppgs = initialize_polya_gamma_samplers()
# Compute the left hand side analytically
loglhs = psi*a - b * np.log1p(np.exp(psi))
# Compute the right hand side with Monte Carlo
omegas = np.ones(M)
ppg.pgdrawvpar(ppgs, b*np.ones(M), np.zeros(M), omegas)
logrhss = -b * np.log(2) + (a-b/2.)*psi -0.5 * omegas*psi**2
logrhs = logsumexp(logrhss) - np.log(M)
print("Monte Carlo")
print("log LHS: ", loglhs)
print("log RHS: ", logrhs)
