sklearn_gmm.py

from sklearn.mixture import GaussianMixture
sklearn_pca = PCA(n_components = 2)
Y_sklearn = sklearn_pca.fit_transform(tf_idf_array)
gmm = GaussianMixture(n_components=3, covariance_type='full').fit(Y_sklearn)
prediction_gmm = gmm.predict(Y_sklearn)
probs = gmm.predict_proba(Y_sklearn)

centers = np.zeros((3,2))
for i in range(3):
    density = mvn(cov=gmm.covariances_[i], mean=gmm.means_[i]).logpdf(Y_sklearn)
    centers[i, :] = Y_sklearn[np.argmax(density)]

plt.figure(figsize = (10,8))
plt.scatter(Y_sklearn[:, 0], Y_sklearn[:, 1],c=prediction_gmm ,s=50, cmap='viridis')
plt.scatter(centers[:, 0], centers[:, 1],c='black', s=300, alpha=0.6);