gmmPlotEllipses

import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture
from sklearn.datasets import make_blobs
from matplotlib.patches import Ellipse
import os

X, _ = make_blobs(n_samples=300, centers=3, random_state=42)
gmm = GaussianMixture(n_components=3, covariance_type='full', random_state=42)
gmm.fit(X)

labels = gmm.predict(X)
means = gmm.means_
covs = gmm.covariances_

fig, ax = plt.subplots()

ax.scatter(X[:, 0], X[:, 1], c=labels, s=10)

for mean, cov in zip(means, covs):
    vals, vecs = np.linalg.eigh(cov)
    order = vals.argsort()[::-1]
    vals, vecs = vals[order], vecs[:, order]
    theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
    width, height = 2 * np.sqrt(vals)
    ell = Ellipse(xy=mean, width=width, height=height, angle=theta, edgecolor='black', facecolor='none')
    ax.add_patch(ell)

path = '/tmp/tvtoGmmPlotEllipses.png'
plt.savefig(path, dpi=150, bbox_inches='tight')
os.system(f'xdg-open {path}')