## Viterbi Algorithm in NLP## What is Viterbi Algorithm?The The This formula will find the states that can We can use the given formula to find the This is the mu function, and it works with the first formula. The technique has widespread use in ## Hidden Markov Model ( HMM )The It predicts future observations based on the hidden process responsible for generating data. It has two types of variables: - Hidden State: The hidden states are those variables used to produce the hidden but observed data
- Observation: The variables which are to be measured and observed are the observations
## Hidden Markov Model and Viterbi AlgorithmThe ## How does the Viterbi Algorithm Work?The Let's understand the Viterbi Algorithm with the help of a program in Python. ## Program 1: Program to illustrate the Viterbi Algorithm and Hidden Markov Model in Python.Problem Statement: Here, we are provided with the weather data. We have to predict the weather conditions for the future with the help of the current weather conditions.
We have imported numpy, matplotlib, seaborn for visualization and hmmlearn for the predictions.
Number of hidden states : 3 Number of observations : 3
We have initialized different state space, which includes Sunny, Rainy, and Winter. Then, we have defined the observation space, which has different observations like Dry, Wet, and Humid.
The State probability: [0.5 0.4 0.1] The Transition probability: [[0.2 0.3 0.5] [0.3 0.4 0.3] [0.5 0.3 0.2]] The Emission probability: [[0.2 0.1 0.7] [0.2 0.5 0.3] [0.4 0.2 0.4]
We have defined the initial state probability, transition probability, and emission probability in arrays. The state probability defines the probability of the beginning in every hidden state. The transition probability defines the probability of transitioning from one state to another. The emission probability defines the probability of checking the observations in each hidden state.
We have made the HMM model using the categoricalHMM. The model's parameters are set with the state probability, transition, and emission probability.
array([[1], [1], [0], [1], [1]])
The observed data is defined in a sequence of a numpy array representing different observations.
The Most likely hidden states are: [1 1 2 1 1]
We have predicted the most likely sequence of the hidden states using the HMM model.
Log Probability : -8.845697258388274 Most likely hidden states: [1 1 2 1 1]
Using the
We have plotted a graph displaying the results between the period and the hidden states. |

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