def metropolis_pb(ks,q,verbose=False,mu_offset=0,iterations=50000):
"""Metropolis-Hastings sampling for ks, given product-bernoulli proposal function"""
G = len(ks)
eps = [-log(k) for k in ks]
f = lambda mu:sum(fd(ep,mu) for ep in eps) - q
mu = bisect_interval(f,-50,50) + mu_offset
def weight(ss):
return (falling_fac(q,sum(ss))*product(k**s for k,s in zip(ks,ss)))
def proposal(ss):
#state = [int(random.random() < p) for _ in range(len(ss))]
state = rstate(eps,mu)
#print "proposed state with occ:",sum(state)
return state
def dprop(ss):
prop = dstate(ss,eps,mu)
#print "prop:",prop
return prop
x0 = proposal([0] * len(ks))
return mh(weight,proposal,x0,dprop=dprop,verbose=verbose,iterations=iterations)
def metropolis_uniform(ks,q,verbose=False,mu_offset=0,iterations=50000):
"""Metropolis-Hastings sampling for ks, given uniform proposal function"""
G = len(ks)
eps = [-log(k) for k in ks]
f = lambda mu:sum(fd(ep,mu) for ep in eps) - q
mu = bisect_interval(f,-50,50) + mu_offset
def weight(ss):
return (falling_fac(q,sum(ss))*product(k**s for k,s in zip(ks,ss)))
def proposal(ss):
on_chr_prob = sum(ss)/float(q)
on_chr = random.random() < on_chr_prob
ss_new = ss[:]
if on_chr:
pos = random.choice([i for (i,s) in enumerate(ss) if s])
ss_new[pos] = 0
new_pos = random.choice([-1] + [i for (i,s) in enumerate(ss) if not s])
if new_pos >= 0:
ss_new[new_pos] = 1
return ss_new
x0 = proposal([0] * len(ks))
return mh(weight,proposal,x0,verbose=verbose,iterations=iterations)