#1 Implementation of Hidden Markov Model with Gaussian Output

[Anshul Dube]

This was my course project. Hope it is of some use.
This is implementation of Hidden Markov Model, the states emit output based on a multi-variate Gaussian distribution. The parameters required can be set in the main file. When the program is run it takes the training sequences from the training data and trains a HMM model for each class using Baum-Welch Algorithm, it calculates the log likelihood value based on the Viterbi Algorithm. Finally it generates a confusion matrix of classified examples.

It needs a HMM toolkit for operating, which can be found at: http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
This package is also open source.

[Anshul Dube]