I want to use **Gaussian Mixture Models initiated with K-Means** to do **cluster analysis** for a data set with 6 features, i.e. **unsupervised learning to detect potential classes, or groups, in the data set**.

I know how to do this through Gaussian Mixture Models in Scikit-Learn, as shown below:

```
# init GMM with K-Means
gm_kmeans = GaussianMixture(
n_components = 30,
max_iter= 1000,
tol = 1e-4,
init_params= 'kmeans',
)
# predicated class
y = gm_kmeans.fit_predict(data.iloc[:, 2:8])
# predicted probabilities belonging to each of the classes
y_proba = gm_kmeans.predict_proba(data.iloc[:, 2:8])
```

However, this algorithm usually takes a long time to calculate via CPU, and Scikit-Learn is not designed to utilize GPU for parallel processing in this regard.

So, I’d like to ask if there is a PyTorch equivalent to this algorithm. Or, how to implement Gaussian Mixture Models init with K-Means for unsupervised classification that can utilize GPU.

Homework I did: