https://github.com/preethi2805/gaussian_mixture_models
This repository contains the implementation of a 3-component, 2D Gaussian Mixture Model (GMM) using Python. The project generates and visualizes samples from the GMM, displaying the joint distribution, marginal distribution, and the responsibility of each component in explaining the data.
https://github.com/preethi2805/gaussian_mixture_models
Last synced: 16 days ago
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This repository contains the implementation of a 3-component, 2D Gaussian Mixture Model (GMM) using Python. The project generates and visualizes samples from the GMM, displaying the joint distribution, marginal distribution, and the responsibility of each component in explaining the data.
- Host: GitHub
- URL: https://github.com/preethi2805/gaussian_mixture_models
- Owner: Preethi2805
- Created: 2025-02-20T04:02:11.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-20T04:17:21.000Z (over 1 year ago)
- Last Synced: 2025-03-06T13:41:36.321Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 221 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Gaussian Mixture Models
## Objective
This repository contains an implementation of **Exercise 4** from the *Pattern Recognition and Machine Learning* textbook. The goal of the exercise is to visualize the behaviour of a **3-component, 2D Gaussian Mixture Model (GMM)** using the specified parameters for each Gaussian component.
The exercise includes generating samples from the GMM, and visualizing them in a 3-panel plot that shows:
1. **Joint Distribution** of the GMM samples.
2. **Marginal Distribution** of the samples.
3. **Responsibilities** of each Gaussian component in explaining each sample.
## Files in this Repository
- **gmm_visualization.py**: Contains the implementation of the Gaussian Mixture Model sampling, visualization, and plotting.
- **README.md**: This file provides an overview of the exercise and repository.
## Conclusion
The code successfully implements the task to visualize a 3-component 2D Gaussian Mixture Model, with appropriate visualization techniques to display how each component contributes to the overall mixture. The plot allows for a qualitative analysis of the GMM's behavior and the responsibilities of each component in explaining the data.