{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"hmmlearn implements unsupervised learning and inference algorithms for Hidden Markov Models with a scikit-learn compatible API.","skillfed_tags":["sequence-modeling","unsupervised-learning","markov-models"],"use_cases":["Train HMM models on unlabeled time-series data to discover hidden state patterns in sensor readings or financial data.","Decode the most likely sequence of hidden states given observed data using Viterbi algorithm.","Model biological sequences (DNA, protein) where true state labels are unknown but state transitions follow Markovian structure.","Estimate transition and emission probabilities from sequence data for downstream inference tasks.","Prototype sequence modeling experiments before moving to more complex approaches."],"what_it_does":"hmmlearn provides a suite of algorithms for training and using Hidden Markov Models without labeled data. It wraps the mathematical machinery of HMM inference\u2014forward-backward algorithms, Viterbi decoding, and parameter estimation\u2014behind a scikit-learn-style interface, so if you've used scikit-learn's fit/predict pattern, the API will feel familiar. The package depends on numpy, scikit-learn, and scipy for its numerical and statistical foundations.\n\nTypical use involves fitting an HMM to sequence data (like time series or biological sequences) to learn the underlying state structure, then using that model to decode hidden states or generate predictions. It's built for research and production work on sequence modeling tasks where you don't have ground-truth labels for the states themselves.","worth_installing":"Yes, if you need unsupervised HMM modeling and can accept dormant maintenance. The package is stable, has no known vulnerabilities, and the scikit-learn API integration is well-established. Install friction is moderate (C compiler required) but manageable. Best suited for research, prototyping, or production systems where HMM is the right model and you don't expect active feature development."},"id":"hmmlearn","links":{"html":"https://skillfed.io/packages/hmmlearn","md":"https://skillfed.io/packages/hmmlearn.md","pypi":"https://pypi.org/project/hmmlearn/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-10-31","license_spdx":null,"license_treatment":"permissive","name":"hmmlearn","python_support":"supports_current","summary":"Hidden Markov Models in Python with scikit-learn like API"},"popularity":{"monthly_downloads":1269719,"position":4129,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.3"}