def astep(self,
q0: RaveledVars) -> Tuple[RaveledVars, List[Dict[str, Any]]]:
point_map_info = q0.point_map_info
q0 = q0.data
if not self.steps_until_tune and self.tune:
# Tune scaling parameter
self.scaling = tune(self.scaling,
self.accepted_sum / float(self.tune_interval))
# Reset counter
self.steps_until_tune = self.tune_interval
self.accepted_sum[:] = 0
delta = self.proposal_dist() * self.scaling
if self.any_discrete:
if self.all_discrete:
delta = np.round(delta, 0).astype("int64")
q0 = q0.astype("int64")
q = (q0 + delta).astype("int64")
else:
delta[self.discrete] = np.round(delta[self.discrete], 0)
q = q0 + delta
else:
q = floatX(q0 + delta)
if self.elemwise_update:
q_temp = q0.copy()
# Shuffle order of updates (probably we don't need to do this in every step)
np.random.shuffle(self.enum_dims)
for i in self.enum_dims:
q_temp[i] = q[i]
accept_rate_i = self.delta_logp(q_temp, q0)
q_temp_, accepted_i = metrop_select(accept_rate_i, q_temp, q0)
q_temp[i] = q_temp_[i]
self.accept_rate_iter[i] = accept_rate_i
self.accepted_iter[i] = accepted_i
self.accepted_sum[i] += accepted_i
q = q_temp
else:
accept_rate = self.delta_logp(q, q0)
q, accepted = metrop_select(accept_rate, q, q0)
self.accept_rate_iter = accept_rate
self.accepted_iter = accepted
self.accepted_sum += accepted
self.steps_until_tune -= 1
stats = {
"tune": self.tune,
"scaling": np.mean(self.scaling),
"accept": np.mean(np.exp(self.accept_rate_iter)),
"accepted": np.mean(self.accepted_iter),
}
return RaveledVars(q, point_map_info), [stats]
def astep(self,
q0: RaveledVars) -> Tuple[RaveledVars, List[Dict[str, Any]]]:
point_map_info = q0.point_map_info
q0 = q0.data
if not self.steps_until_tune and self.tune:
# Tune scaling parameter
self.scaling = tune(self.scaling,
self.accepted / float(self.tune_interval))
# Reset counter
self.steps_until_tune = self.tune_interval
self.accepted = 0
delta = self.proposal_dist() * self.scaling
if self.any_discrete:
if self.all_discrete:
delta = np.round(delta, 0).astype("int64")
q0 = q0.astype("int64")
q = (q0 + delta).astype("int64")
else:
delta[self.discrete] = np.round(delta[self.discrete], 0)
q = q0 + delta
else:
q = floatX(q0 + delta)
accept = self.delta_logp(q, q0)
q_new, accepted = metrop_select(accept, q, q0)
self.accepted += accepted
self.steps_until_tune -= 1
stats = {
"tune": self.tune,
"scaling": self.scaling,
"accept": np.exp(accept),
"accepted": accepted,
}
q_new = RaveledVars(q_new, point_map_info)
return q_new, [stats]