def filter(self, data, input=None, mask=None, tag=None):
m = self.state_map
pi0 = self.init_state_distn.initial_state_distn
Ps = self.transitions.transition_matrices(data, input, mask, tag)
log_likes = self.observations.log_likelihoods(data, input, mask, tag)
pzp1 = hmm_filter(replicate(pi0, m), Ps, replicate(log_likes, m))
return collapse(pzp1, m)
def filter(self, data, input=None, mask=None, tag=None):
pi0 = self.init_state_distn.initial_state_distn
Ps = self.transitions.transition_matrices(data, input, mask, tag)
log_likes = self.observations.log_likelihoods(data, input, mask, tag)
return hmm_filter(pi0, Ps, log_likes)
