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)
betas.append(model.beta.copy())
# Check that the PG-Multinomial samples are distributed like the prior
thetas = np.array(thetas)
theta_mean = thetas.mean(0)
theta_std = thetas.std(0)
betas = np.array(betas)
beta_mean = betas.mean(0)
beta_std = betas.std(0)
# Now sample from the prior for comparison
print("Sampling from prior")
from pybasicbayes.distributions import GaussianFixedMean
from pgmult.internals.utils import compute_uniform_mean_psi, psi_to_pi
mu, sigma0 = compute_uniform_mean_psi(T)
psis_prior = np.array(
[GaussianFixedMean(mu=mu, lmbda_0=T * sigma0, nu_0=T).rvs(1)
for _ in xrange(N_iter)])
thetas_prior = psi_to_pi(psis_prior[:,0,:])
betas_prior = np.random.dirichlet(alpha_beta*np.ones(V), size=(N_iter,))
# print "Mean psi: ", psi_mean, " +- ", psi_std
import pybasicbayes.util.general as general
percentilecutoff = 5
def plot_1d_scaled_quantiles(p1,p2,plot_midline=True):
# scaled quantiles so that multiple calls line up
p1.sort(), p2.sort() # NOTE: destructive! but that's cool
xmin,xmax = general.scoreatpercentile(p1,percentilecutoff), \
general.scoreatpercentile(p1,100-percentilecutoff)
