Alibi explains machine learning model predictions through a set of algorithms.
Gaussian Mixture Model
Unsupervised machine learning with multivariate Gaussian mixture model.
About Gaussian Mixture Model
Gaussian Mixture Model is an open-source library for unsupervised learning with multivariate Gaussian mixture models. It supports both offline data and real-time data streams.
It installs via npm and is initialized with weights, means, and covariances. Points are added with addPoint, trained with EM iterations using runEM, and scored with predict and predictNormalize.
Key features
- Supports offline data and real-time data stream
- Installs via npm as gaussian-mixture-model
- Initializes model with weights, means, covariances
- Adds data points to model with addPoint
- Runs EM algorithm for specified iterations
- Predicts cluster probabilities with predict and predictNormalize
Best for: Developers who need to add multivariate Gaussian mixture clustering to an application.
Good for
- Released under MIT license
- Supports offline data and real-time data streams
- Provides live demo for testing behavior
Gaussian Mixture Model at a glance
- Category
- AI & Machine Learning › Models & ML Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Gaussian Mixture Model FAQ
How do I install the package?
Run npm install gaussian-mixture-model.
How do I train the model and make predictions?
Add points with addPoint, run EM iterations with runEM, then use predict or predictNormalize.
Is there a demo available?
Yes, at https://lukapopijac.github.io/gaussian-mixture-model.
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