def run(num_vars, num_values, num_missing, polytree, p):
bn = multinomial.random_network(num_vars, num_values, polytree=polytree, p=p)
z, missing = random_var(num_vars, num_values, num_missing=num_missing)
x = bn.var(npr.choice(missing))
# if len(missing) == 1:
# print bn
# print x.dist(z)
print "exact",
i_exact = inference.enumeration_ask(bn, x, np.array(z))
print "likelihood",
i_likel = inference.likelihood_weighting(bn, x, np.array(z))
lr1 = likelihood_ratio(i_exact, i_likel)
print "%6.4f" % (lr1,),
print "gibbs",
i_gibbs = inference.gibbs(bn, x, np.array(z))
lr2 = likelihood_ratio(i_exact, i_gibbs)
print "%6.4f" % (lr2,)
return lr1, lr2
