import numpy as np
import pymc as pm
from pymc_submodels import infty_mixture_model as imm

data = np.array([10., 11., 12., -10., -11., -12.])
# Precision of Gaussian likelihood
tau_like = 0.5
# Mean and precision of Gaussian base distribution
mu_base = 0. 
tau_base = 0.01

# Concentration parameter of Dirichlet process
alpha = 1. 

# Construct model using pymc_submodels package
mdl = imm.model(data, tau_like, mu_base, tau_base, alpha)

# Call PyMC
mcmc = pm.MCMC(mdl)
# Sample
mcmc.sample(10000, 1000, 2)   
# Stochastic 'z' component membership of each data point
print np.mean(mcmc.trace('z')[:], 0)