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Published 20 hours ago • verified publisher icon fmorenopino
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Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types...

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Learning Rate for Multinomial HMM

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Hi all, Thanks for developing this tool! It is quite useful that it handles missing data unlike hmmlearn. I was playing with the Multinomial HMM. I noticed that the learning...

tahaceritli avatar tahaceritli

Divergences in utils.py

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Hi all! I noticed a small divergence in the `utils.py` file when installing this lib from PyPi and GitHub - in this case, when setting the style for the charts....

giancarloyona avatar giancarloyona

vectorized version

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Hi guys, I think that your library is super useful. Especially being able to combine continuous and discrete features. It is however not vectorized with respect to the number of...

nicolasviboo avatar nicolasviboo

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Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types...

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