def resample_states(self,**kwargs):
# NOTE: kwargs is just to absorb any multiprocessing stuff
# TODO only use this when the number/size of sequences warrant it
from messages import resample_arhmm
assert self.obs_distns[0].D_out > 1
if len(self.states_list) > 0:
stateseqs = [np.empty(s.T,dtype='int32') for s in self.states_list]
params, normalizers = map(np.array,zip(*[self._param_matrix(o) for o in self.obs_distns]))
params, normalizers = params.repeat(s.rs,axis=0), normalizers.repeat(s.rs,axis=0)
stats, _, loglikes = resample_arhmm(
[s.hmm_pi_0.astype(self.dtype) for s in self.states_list],
[s.hmm_trans_matrix.astype(self.dtype) for s in self.states_list],
params.astype(self.dtype), normalizers.astype(self.dtype),
[undo_AR_striding(s.data,self.nlags) for s in self.states_list],
stateseqs,
[np.random.uniform(size=s.T).astype(self.dtype) for s in self.states_list],
self.alphans)
for s, stateseq, loglike in zip(self.states_list,stateseqs,loglikes):
s.stateseq = stateseq
s._map_states()
s._normalizer = loglike
starts, ends = cumsum(s.rs,strict=True), cumsum(s.rs,strict=False)
stats = map(np.array,stats)
stats = [sum(stats[start:end]) for start, end in zip(starts,ends)]
self._obs_stats = stats
else:
self._obs_stats = None
def resample_states(self, **kwargs):
from messages import resample_arhmm
if len(self.states_list) > 0:
stateseqs = [
np.empty(s.T, dtype='int32') for s in self.states_list
]
params, normalizers = map(
np.array,
zip(*[self._param_matrix(o) for o in self.obs_distns]))
stats, transcounts, loglikes = resample_arhmm(
[s.pi_0.astype(self.dtype) for s in self.states_list],
[s.trans_matrix.astype(self.dtype) for s in self.states_list],
params.astype(self.dtype), normalizers.astype(self.dtype), [
undo_AR_striding(s.data, self.nlags)
for s in self.states_list
], stateseqs, [
np.random.uniform(size=s.T).astype(self.dtype)
for s in self.states_list
], self.alphans)
for s, stateseq, loglike in zip(self.states_list, stateseqs,
loglikes):
s.stateseq = stateseq
s._normalizer = loglike
self._obs_stats = stats
self._transcounts = transcounts
else:
self._obs_stats = None
self._transcounts = []
def resample_states(self,**kwargs):
from messages import resample_arhmm
if len(self.states_list) > 0:
stateseqs = [np.empty(s.T,dtype='int32') for s in self.states_list]
params, normalizers = map(np.array,zip(*[self._param_matrix(o) for o in self.obs_distns]))
stats, transcounts, loglikes = resample_arhmm(
[s.pi_0.astype(self.dtype) for s in self.states_list],
[s.trans_matrix.astype(self.dtype) for s in self.states_list],
params.astype(self.dtype), normalizers.astype(self.dtype),
[undo_AR_striding(s.data,self.nlags) for s in self.states_list],
stateseqs,
[np.random.uniform(size=s.T).astype(self.dtype) for s in self.states_list],
self.alphans)
for s, stateseq, loglike in zip(self.states_list,stateseqs,loglikes):
s.stateseq = stateseq
s._normalizer = loglike
self._obs_stats = stats
self._transcounts = transcounts
else:
self._obs_stats = None
self._transcounts = []
