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