Simple Implementation of the Viterbi Algorithm for training Hidden Markov Models.

This is an implementation of the Viterbi Algorithm for training Hidden Markov models based on Luis Serrano's YouTube video on the subject. This repo accompanies the video found here: https://www.youtube.com/watch?v=kqSzLo9fenk

This implementation can handle prior probabilities, and any sized probability transition matrix. It cannot handle exit probabilities though.

Example sets

Choose any one of the function names in the example_sets class file and use it like so in the main:

example_sets.function_name()

Open in Google Colab

| Lab | Description | Open in Google Colab | |-----|-------------|----------------------| | Simple_HMM.ipynb | Code a Hidden Markov Model from scratch | |

✅ Tip: Click any “Open in Colab” button to launch the lab in Google Colab. From there, you can run the notebook in the cloud, make edits, and save your changes back to your own Drive.

Acknowledgement

Thanks so much to Daniel Hernandez for all his help!