yahmm
This machine learning software implements Hidden Markov Models (HMMs) through a flexible, graph-based interface, allowing users to construct models incrementally. It offers functionalities for training and evaluating sequences, utilizing various algorithms like Baum-Welch and Viterbi. Though development has shifted to pomegranate, yahmm remains a robust tool for HMM applications.
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Top yahmm Features
- Compositional graph-based interface
- Node-by-node model construction
- Edge-by-edge transitions
- Efficient probability calculations
- Supports tied states functionality
- Custom distribution creation
- Integrates with pomegranate library
- Silent states management
- Model concatenation capabilities
- Viterbi and Baum-Welch training
- Easy installation via PyPi
- Log probability calculations
- Full training iteration logging
- Supports multiple emission distributions
- Model state visualization
- Serialization and deserialization support
- Robust feedback integration
- Active community contributions
- Comprehensive documentation and examples.
