kylesayrs/GMMPytorch
Pytorch implementation of same-family gaussian mixture models with guardrails. Features separable parameter optimization and singularity mitigation
This project helps data analysts and machine learning engineers understand the underlying structure of their datasets by identifying distinct clusters of data points. You input raw, multi-dimensional data, and it outputs the parameters (means, covariances) of several Gaussian distributions, along with the probability each data point belongs to each cluster. This allows for data segmentation, anomaly detection, or foundational analysis for more complex models.
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Use this if you need to identify natural groupings within your numerical data, understand the statistical properties of those groups, and assign probabilities of membership to each group for every data point.
Not ideal if your data is purely categorical, you need to cluster text or image data without prior numerical representation, or if you are looking for a simple, non-probabilistic clustering method.
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26
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2
Language
Python
License
MIT
Category
Last pushed
May 31, 2025
Commits (30d)
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