第09章 EM算法及其推广 - 习题9.3 - 可视化

import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

np.random.seed(0)
X = np.concatenate([np.random.normal(0, 1, 100), np.random.normal(5, 1, 100)]).reshape(-1, 1)
gmm = GaussianMixture(n_components=2)
gmm.fit(X)
labels = gmm.predict(X)

plt.hist(X[labels==0], bins=20, alpha=0.5, label='Cluster 0')
plt.hist(X[labels==1], bins=20, alpha=0.5, label='Cluster 1')
plt.legend()