def step(self):
# TODO: Propose from not the prior, and tune using the asf's.
# The right-hand sides for the linear constraints
self.rhs = dict(zip(self.constraint_offdiags,
[np.asarray(np.dot(pm.utils.value(od), self.g.value)).squeeze() for od in self.constraint_offdiags]))
this_round = np.zeros(self.n, dtype='int')
for i in xrange(self.n):
self.check_constraints()
# Jump an element of g.
lb, ub, rhs = self.get_bounds(i)
# Propose a new value
curg = self.g.value[i]
tau = 1./self.adaptive_scale_factor[i]**2
newg = pm.rtruncnorm(curg,tau,lb,ub)[0]
# The Hastings factor
hf = pm.truncnorm_like(curg,newg,tau,lb,ub)-pm.truncnorm_like(newg,curg,tau,lb,ub)
# The difference in prior log-probabilities of g
dpri = .5*(curg**2 - newg**2)
# Get the current log-likelihood of the non-constraint children.
lpl = self.get_likelihood_only()
cv = {}
for od in self.all_offdiags:
for c in od.children:
cv[c] = c.value.copy()
# Inter the proposed value and get the proposed log-likelihood.
self.set_g_value(newg, i)
try:
lpl_p = self.get_likelihood_only()
except pm.ZeroProbability:
self.reject(i, cv)
self.check_constraints()
this_round[i] = -1
continue
# M-H acceptance
if np.log(np.random.random()) < lpl_p - lpl + hf + dpri:
self.accepted[i] += 1
this_round[i] = 1
for od in self.constraint_offdiags:
rhs[od] += np.asarray(pm.utils.value(od))[:,i].squeeze() * newg
self.rhs = rhs
self.check_constraints()
else:
self.reject(i, cv)
self.check_constraints()
this_round[i] = -1
def hastings_factor(self):
tau = 1.0 / (self.adaptive_scale_factor * self.proposal_sd) ** 2
cur_val = self.stochastic.value
last_val = self.stochastic.last_value
lp_for = pm.truncnorm_like(cur_val, last_val, tau, self.low_bound, self.up_bound)
lp_bak = pm.truncnorm_like(last_val, cur_val, tau, self.low_bound, self.up_bound)
if self.verbose > 1:
print self._id + ": Hastings factor %f" % (lp_bak - lp_for)
return lp_bak - lp_for
def hastings_factor(self):
tau = 1./(self.adaptive_scale_factor * self.proposal_sd)**2
cur_val = self.stochastic.value
last_val = self.stochastic.last_value
lp_for = pm.truncnorm_like(cur_val, last_val, tau, self.low_bound, self.up_bound)
lp_bak = pm.truncnorm_like(last_val, cur_val, tau, self.low_bound, self.up_bound)
if self.verbose > 1:
print self._id + ': Hastings factor %f'%(lp_bak - lp_for)
return lp_bak - lp_for
