At this point, we can continue with the previous example, using our model to find the most likely hidden state sequence given a set of possible observations. We can use either the decode() method or the predict() method. The first one returns the log probability of the whole sequence and the sequence itself; however, they all use the Viterbi algorithm as a default decoder:

sequence = np.array([[1], [1], [1], [0], [1], [1], [1], [0], [1], 
                     [0], [1], [0], [1], [0], [1], [1], [0], [1], 
                     [1], [0], [1], [0], [1], [0], [1], [0], [1], 
                     [1], [1], [0], [0], [1], [1], [0], [1], [1]], dtype=np.int32)

lp, hs = hmm_model.decode(sequence)

print(hs)
[0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1]

print(lp)
-30.489992468878615

The sequence is coherent with the transition probability matrix; in fact, it's more likely the persistence of rough weather (1) than the opposite. As a consequence, the transition from 1 to X is less likely than the one from 0 to 1. The choice of state is made by selecting the highest probability; however, in some cases, the differences are minimal (in our example, it can happen to have p = [0.49, 0.51], meaning that there's a high error chance), so it's useful to check the posterior probabilities for all the states in the sequence:

pp = hmm_model.predict_proba(sequence)
print(pp)

[[  1.00000000e+00   5.05351938e-19]
 [  3.76687160e-05   9.99962331e-01]
 [  1.31242036e-03   9.98687580e-01]
 [  9.60384736e-01   3.96152641e-02]
 [  1.27156616e-03   9.98728434e-01]
 [  3.21353749e-02   9.67864625e-01]
 [  1.23481962e-03   9.98765180e-01]

...

In our case, there are a couple of states that have p ∼ [0.495, 0.505], so even if the output state is 1 (rough weather), it's also useful to consider a moderate probability to observe good weather. In general, if a sequence is coherent with the transition probability previously learned (or manually input), those cases are not very common. I suggest trying different configurations and observations sequences, and to also assess the probabilities for the strangest situations (like a sequence of zero second). At that point, it's possible to retrain the model and recheck the new evidence has been correctly processed.