def loglike(self,data):
warn('untested')
if len(self.states_list) > 0:
s = self.states_list[0] # any state sequence object will work
else:
# we have to create a temporary one just for its methods, though the
# details of the actual state sequence are never used
s = states.hmm_states(len(data),self.state_dim,self.obs_distns,self.trans_distn,
self.init_state_distn,stateseq=np.zeros(len(data),dtype=np.uint8))
aBl = s.get_aBl(data)
betal = s.messages_backwards(aBl)
return np.logaddexp.reduce(np.log(self.initial_distn.pi_0) + betal[0] + aBl[0])
def generate(self,T,keep=True):
'''
Generates a forward sample using the current values of all parameters.
Returns an observation sequence and a state sequence of length T.
If keep is True, the states object created is appended to the
states_list. This is mostly useful for generating synthetic data and
keeping it around in an HSMM object as the latent truth.
To construct a posterior sample, one must call both the add_data and
resample methods first. Then, calling generate() will produce a sample
from the posterior (as long as the Gibbs sampling has converged). In
these cases, the keep argument should be False.
'''
tempstates = states.hmm_states(T,self.state_dim,self.obs_distns,self.trans_distn,
self.init_state_distn)
return self._generate(tempstates,keep)
def add_data(self,data,stateseq=None):
self.states_list.append(states.hmm_states(len(data),self.state_dim,self.obs_distns,self.trans_distn,
self.init_state_distn,data=data,stateseq=stateseq))