Set θ = ( A , B , π ) with initial conditions (model_init.txt)
Forward Procedure:
Compute αi(t) = P( O1=o1, ..., Ot=ot, qt=i | θ ) recursively, the probability of seeing the observation sequence o1, o2, ..., ot and being in state i at time t.
Backward Procedure:
Compute βi(t) = P( Ot+1=ot+1, ..., OT=oT | qt=i, θ ) recursively, the probability of the ending partial observation sequence ot+1, ot+2, ..., oT given starting state i at time t.
Accumulation Procedure:
Calculate the temporary variables, according to Bayes' theorem.
Gamma: the probability of being in state i at time t given the observed sequence O and the parameters θ.
Epsilon: the probability of being in state i and j at times t and t+1 respectively given the observed sequence O and parameters θ.
Update Procedure:
Parameters of the hidden Markov model θ can now be updated: ( A , B , π ) = ( A* , B* , π* )
A dynamic programming algorithm for finding the most likely sequence of hidden states, that results in a sequence of observed events.
Given a hidden Markov model (HMM) with state space Q, initial probabilities πi of being in state i and transition probabilities a(i,j) of transitioning from state i to state j. Say we observe outputs o1, ..., oT. The most likely state sequence q1, ..., qT that produces the observations is given by the Viterbi relations.
This algorithm generates a path Q = ( q1, q2, ..., qT ), which is a sequence of states qt ∈ Q = { q1, q2, ..., qK } that generate the observations O = ( o1, o2, ..., oT ) with on ∈ O = { o1, o2, ..., oN }, N being the count of observations.
File Description
.
├── src/
| ├── Makefile g++ compiler make file
| ├── hmm.h HMM implementation
| ├── hmm.cpp HMM implementation
| ├── test_hmm.cpp Testing algorithm implementation
| ├── train_hmm.cpp Training algorithm implementation
| ├── test Unix executable binary code for test_hmm.cpp (See the next "Usage" section for more details)
| ├── train Unix executable binary code for train_hmm.cpp (See the next "Usage" section for more details)
| ├── plot.py Draws the training plot
| ├── model_01~05.txt Trained models
| └── modellist.txt Model name list
├── data/
| ├── model_init.txt Initial model for training
| ├── seq_model_01~05.txt Training data (observation sequences)
| ├── testing_data1.txt Testing data (observation sequences)
| ├── testing_answer.txt Real answer for "testing_data1.txt"
| ├── testing_result.txt Model generated answer for "testing_data1.txt"
| └── testing_data2.txt Testing data without answer
├──problem_description.pdf Work Spec
└── Readme.md This File
Usage
Train models separately, then test
└── src/
├── make clean
├── make
├── ./train #iteration ../data/model_init.txt ../data/seq_model_01.txt model_01.txt
├── ./train #iteration ../data/model_init.txt ../data/seq_model_02.txt model_02.txt
├── ./train #iteration ../data/model_init.txt ../data/seq_model_03.txt model_03.txt
├── ./train #iteration ../data/model_init.txt ../data/seq_model_04.txt model_04.txt
├── ./train #iteration ../data/model_init.txt ../data/seq_model_05.txt model_05.txt
└── ./test modellist.txt ../data/testing_data1.txt ../data/testing_result1.txt
- #iteration is positive integer, which is the iteration of the Baum-Welch algorithm.
Train all models at once, then test
└── src/
├── make clean
├── make
├── ./train 250 ../data/model_init.txt ../data/ modellist.txt all
└── ./test modellist.txt ../data/testing_data1.txt ../data/testing_result1.txt
- #iteration is positive integer, which is the iteration of the Baum-Welch algorithm.
Train all models at once, along with the calculation of the HMM's test score in every iteration (Suggest Usage)
└── src/
├── make clean
├── make
├── ./train 250 ../data/model_init.txt ../data/ modellist.txt all test
└── ./test modellist.txt ../data/testing_data1.txt ../data/testing_result1.txt
- #iteration is positive integer, which is the iteration of the Baum-Welch algorithm.
Draw Training Plot
└── src/
└── python3 plot.py
Experinment: Iteration v.s. Accuracy Plot
Result: Maximum accuracy 0.8708 achieved at the 2560-th iteration
Training Plot:
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