# 4. Example¶

This notebook illustrates the usage of the functions in this package, for a discrete hidden markov model. In the example below, the HMM has two states ‘s’ and ‘t’. There are two possible observation which are ‘A’ and ‘B’. The start probabilities, emission probabilities and transition probabilities are initialized as below. There are two observation sequences ‘obs1’ and ‘obs2’. The variable ‘quantities_observations’ indicates the number of times, the sequences obs1 and obs2 are seen.

```
# Import required Libraries
import numpy as np
from hidden_markov import hmm
```

## 4.1. Parameter Intialization¶

```
# ====Initializing Parameters ====
# States
states = ('s', 't')
# list of possible observations
possible_observation = ('A','B' )
# The observations that we observe and feed to the model
obs1 = ('A', 'B','B','A')
obs2 = ('B', 'A','B')
# Number of observation sequece 1 and observation sequence 2
quantities_observations = [10, 20]
observation_tuple = []
observation_tuple.extend( [obs1,obs2] )
# Input parameters as Numpy matrices
start_probability = np.matrix( '0.5 0.5 ')
transition_probability = np.matrix('0.6 0.4 ; 0.3 0.7 ')
emission_probability = np.matrix( '0.3 0.7 ; 0.4 0.6 ' )
```

A class object called ‘test’ is initialized with parameters. Note that the parameters are mandatory and default arguments do not exist. Additionally, the start probabilities, transition probabilities and emission probabilites are all numpy matrices. The observations and states are both tuples and the observation_tuples variable is a list of observations.

```
test = hmm(states,possible_observation,start_probability,transition_probability,emission_probability)
```

## 4.2. Forward Algorithm¶

The forward algorithm find the probability of occurence of an observation sequence. The function inputs an observation sequence and returns the probability. The transition, start and emission probabilities used, are the same as those specified in the class object definition.

```
#Forward Algorithm Results on 'obs1'
test.forward_algo(obs1)
```

```
0.051533999999999996
```

## 4.3. Viterbi Algorithm¶

The Viterbi algorithm finds the most probable sequence of states for a given sequence of observations. The function inputs an observation sequence and returns the most probable sequence of states.

```
#Output of the Viterbi algorithm
test.viterbi(obs1)
```

```
['t', 't', 't', 't']
```

## 4.4. Baum-welch Algorithm¶

Using the principles of expectation maximization, the Baum-algorithm finds the emission, start and transition probabilities that represent a list of observation sequences. We use log-probability in order to prevent an overflow error. The function inputs as parameters, a set of observation sequences, the number of times each observation sequence occurs and the number of iterations. The function then returns the final emission, start and transition probabilities.

```
prob = test.log_prob(observation_tuple, quantities_observations)
print ("probability of sequence with original parameters : %f"%(prob))
```

```
probability of sequence with original parameters : -67.920122
```

```
#Sequence on which Baum welch algoritm aws applide on
print(observation_tuple)
print(quantities_observations)
```

```
[('A', 'B', 'B', 'A'), ('B', 'A', 'B')]
[10, 20]
```

```
#Apply Baum-welch Algorithm
num_iter=1000
emission,transition,start = test.train_hmm(observation_tuple,num_iter,quantities_observations)
```

```
#Print output after applying the algorithm
print(emission)
```

```
[[ 0.36193015 0.63806985]
[ 0.41111482 0.58888518]]
```

```
print(start)
```

```
[[ 0.49431308 0.50568692]]
```

```
print(transition)
```

```
[[ 0.58184591 0.41815409]
[ 0.29789921 0.70210079]]
```

Notice that the probability of occurence of an observation sequence has increased for the new model parameters

```
prob = test.log_prob(observation_tuple, quantities_observations)
print ("probability of sequence after %d iterations : %f"%(num_iter,prob))
```

```
probability of sequence after 1000 iterations : -67.356668
```