hidten

A framework that integrates hidden Markov and related models with modern deep learning—revealing hid(den) structure within your ten(sors).

Find the documentation here to get started.

• Provides intuitive classes to easily define latent state graphs and combine emission distributions for both discrete and continuous input tracks. 🧩
• Implements core Hidden Markov Model (HMM) algorithms for training and inference, including Forward–Backward and Viterbi. ⚡
• Enables gradient-based, highly parallel training as well as efficient parallel inference. 🔄
• Pythonic, clean, extensible, and thoroughly tested. ✅

Installation

python -m pip install hidten[tensorflow]

Installation for developers

git clone https://github.com/Gaius-Augustus/hidten pip install -e .[tensorflow,plots,test,doc]

Build the docs:

cd docs && make html

License

This project is licensed under the MIT license.

Tools using hidten

• Tiberius: End-to-End Deep Learning with an HMM for Gene Prediction
• learnMSA: deep protein multiple alignments with large language and hidden Markov models
• bricks2marble: Pre- and postprocessing tools for genome annotation