Python Hidden Markov Models framework
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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 '' 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