def __init__(self,
N,
B=1,
C=3,
pi=10.0,
mu_0=None,
Sigma_0=None,
nu_0=None,
special_case_self_conns=True):
super(SBMGaussianWeightSharedCov, self).\
__init__(N, B=B, C=C, pi=pi,
mu_0=mu_0, Sigma_0=Sigma_0, nu_0=nu_0,
special_case_self_conns=special_case_self_conns)
if mu_0 is None:
mu_0 = np.zeros(B)
if Sigma_0 is None:
Sigma_0 = np.eye(B)
if nu_0 is None:
nu_0 = B + 2
self._cov_model = GaussianFixedMean(mu=np.zeros(B),
nu_0=nu_0,
lmbda_0=Sigma_0)
self._gaussians = [[
GaussianFixedCov(mu_0=mu_0,
sigma_0=np.eye(B),
sigma=self._cov_model.sigma) for _ in xrange(C)
] for _ in xrange(C)]
def __init__(self,
N,
B=1,
dim=2,
b=0.5,
sigma=None,
Sigma_0=None,
nu_0=None,
mu_self=0.0,
eta=0.01):
super(_LatentDistanceModelGaussianMixin, self).__init__(N, B)
self.B = B
self.dim = dim
self.b = b
self.eta = eta
self.L = np.sqrt(eta) * np.random.randn(N, dim)
if Sigma_0 is None:
Sigma_0 = np.eye(B)
if nu_0 is None:
nu_0 = B + 2
self.cov = GaussianFixedMean(mu=np.zeros(B),
sigma=sigma,
lmbda_0=Sigma_0,
nu_0=nu_0)
# Special case self-weights (along the diagonal)
self._self_gaussian = Gaussian(mu_0=mu_self * np.ones(B),
sigma_0=Sigma_0,
nu_0=nu_0,
kappa_0=1.0)
def __init__(self,
N,
B=1,
dim=2,
b=0.5,
sigma=None,
Sigma_0=None,
nu_0=None,
mu_self=0.0,
eta=0.01):
"""
Initialize SBM with parameters defined above.
"""
super(LatentDistanceGaussianWeightDistribution, self).__init__(N)
self.B = B
self.dim = dim
self.b = b
self.eta = eta
self.L = np.sqrt(eta) * np.random.randn(N, dim)
if Sigma_0 is None:
Sigma_0 = np.eye(B)
if nu_0 is None:
nu_0 = B + 2
self.cov = GaussianFixedMean(mu=np.zeros(B),
sigma=sigma,
lmbda_0=Sigma_0,
nu_0=nu_0)
# Special case self-weights (along the diagonal)
self._self_gaussian = Gaussian(mu_0=mu_self * np.ones(B),
sigma_0=Sigma_0,
nu_0=nu_0,
kappa_0=1.0)
# 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.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 range(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)
ymin,ymax = general.scoreatpercentile(p2,percentilecutoff), \
general.scoreatpercentile(p2,100-percentilecutoff)
