A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.
The Gaussian Mixture Model (GMM) is a probabilistic model that represents a mixture of Gaussian (or normal) distributions. It is commonly used for clustering and density estimation tasks.
In Python, the Gaussian Mixture Model can be implemented using the sklearn.mixture module from the scikit-learn library.
To use GMM in Python:
Import the required libraries: from sklearn.mixture import GaussianMixture
Prepare your data: Ensure that your data is in the appropriate format. The GMM expects a 2D array-like object, where each row represents a sample, and each column represents a feature.
Create an instance of the GaussianMixture class with the desired number of components (clusters) and other parameters.
Fit the GMM to your data using the fit() method.
Once the GMM is fitted, you can perform various operations. For example, you can predict cluster labels for new data points using the predict() method, access the learned parameters of the GMM (such as the means, covariances, and weights of the Gaussian components), or generate new samples from the GMM using the sample() method.
These are just a few examples of what you can do with the Gaussian Mixture Model in Python. The scikit-learn documentation provides more detailed information on the available methods and parameters.