Hidden Markov Model and Viterbi Decoding

Data structures for HMM and the Vterbi algorithm in Python.

Prediction

Viterbi decoding algorithm predicts the most probable sequence of hidden states given observations from hmmdata The decoding accuracy is evaluated by using the true sequence of hidden states from hmmdata.

Simulation (Emission)

Markov Model class has a simulate method that can be used to output a sequence of emissions. Sample output:

(0, 'St1', 'b')
..
(4, 'St1', 'c')
(5, 'St1', 'b')
..
(32, 'St2', 'a')
(33, 'St2', 'a')
(34, 'St2', 'b')
(35, 'St2', 'b')
(36, 'St1', 'b')
...

Results

confusion matrix:

St1	        St2
212         6
7           175

true positives:

St1	        St2
212.        175.

true negatives:

St1	        St2
175.        212.

false positives:

St1	        St2
7.          6.

false negatives:

St1	        St2
6.          7.

precision:

St1	        St2
0.96803653  0.96685083

recall:

St1	        St2
0.97247706  0.96153846

fscore:

St1	        St2
0.97025172  0.96418733

support:

St1	        St2
218         182