HMM

A numpy/python-only Hidden Markov Models framework. No other dependencies are required.

This implementation (like many others) is based on the paper: "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, LR RABINER 1989"

Major supported features:

• Discrete HMMs
• Continuous HMMs - Gaussian Mixtures
• Supports a variable number of features
• Easily extendable with other types of probablistic models (simply override the PDF. Refer to 'GMHMM.py' for more information)
• Non-linear weighing functions - can be useful when working with a time-series

Open concerns:

• Examples are somewhat out-dated
• Convergence isn't guaranteed when using certain weighing functions