# jmschrei/pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

Repository: https://github.com/jmschrei/pomegranate
Canonical: https://ross.abutalabs.com/products/pomegranate
Homepage: http://pomegranate.readthedocs.org/en/latest/
Language: Python
License: MIT
License Family: permissive
Topics: python, machine-learning, probabilistic-graphical-models, pytorch
Last push: 2025-03-06T03:50:26+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 10, release rhythm 8, longevity 100
- inputs: {"age_days": 4300, "days_push": 545, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3542, forks 599 (observed 2026-08-28T04:08:09.271834+00:00)

## What it is
Pomegranate is a Python library for fast and flexible probabilistic modeling, built on PyTorch. It supports distributions, mixture models, Bayesian networks, and hidden Markov models that compose modularly with GPU acceleration.

## Use cases
- fit a gaussian mixture model in python
- train a hidden markov model for sequences
- build a bayesian network from data
- probabilistic modeling with gpu acceleration
- create a mixture of gamma and poisson distributions
- classify sequences with hmm-based classifiers

## When to choose
- you need composable probabilistic models like mixtures, Bayesian networks, or HMMs
- you want PyTorch-backed speed, GPU support, and mixed precision
- you want models treated as probability distributions that nest inside each other

## When to avoid
- you rely on the pre-1.0 Cython API, which is incompatible
- you only need general deep learning without probabilistic modeling
- you need a non-Python environment

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, math
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: probabilistic-modeling, pytorch, bayesian-networks, hidden-markov-models, mixture-models, probability-distributions, algorithms, gpu

## Member repositories
- jmschrei/pomegranate (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.271834+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:34:28.688406+00:00, confidence not recorded.
  - readme: https://github.com/jmschrei/pomegranate (fetched 2026-08-28T04:08:09.271834+00:00, sha c327ad3dbde8)
- Data as of 2026-08-30T08:39:29.467469+00:00.