def most_likely_states(self, data, input=None, mask=None, tag=None):
log_pi0 = self.init_state_distn.log_initial_state_distn(
data, input, mask, tag)
log_Ps = self.transitions.log_transition_matrices(
data, input, mask, tag)
log_likes = self.observations.log_likelihoods(data, input, mask, tag)
return viterbi(log_pi0, log_Ps, log_likes)
def most_likely_states(self, data, input=None, mask=None, tag=None):
m = self.state_map
log_pi0 = self.init_state_distn.log_initial_state_distn(data, input, mask, tag)
log_Ps = self.transitions.log_transition_matrices(data, input, mask, tag)
log_likes = self.observations.log_likelihoods(data, input, mask, tag)
z_star = viterbi(replicate(log_pi0, m), log_Ps, replicate(log_likes, m))
return self.state_map[z_star]
def most_likely_states(self,
variational_mean,
data,
input=None,
mask=None,
tag=None):
log_pi0 = self.init_state_distn.log_initial_state_distn(
variational_mean, input, mask, tag)
log_Ps = self.transitions.log_transition_matrices(
variational_mean, input, mask, tag)
log_likes = self.dynamics.log_likelihoods(
variational_mean, input, np.ones_like(variational_mean,
dtype=bool), tag)
log_likes += self.emissions.log_likelihoods(data, input, mask, tag,
variational_mean)
return viterbi(log_pi0, log_Ps, log_likes)
