以防将来有人对同样的事情感到疑惑：一个人有 要使单个组件（而不是总和）标准化，请执行以下操作：
import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture
# Define simple gaussian
def gauss_function(x, amp, x0, sigma):
return amp * np.exp(-(x - x0) ** 2. / (2. * sigma ** 2.))
# Generate sample from three gaussian distributions
samples = np.random.normal(-0.5, 0.2, 2000)
samples = np.append(samples, np.random.normal(-0.1, 0.07, 5000))
samples = np.append(samples, np.random.normal(0.2, 0.13, 10000))
# Fit GMM
gmm = GaussianMixture(n_components=3, covariance_type="full", tol=0.001)
gmm = gmm.fit(X=np.expand_dims(samples, 1))
# Evaluate GMM
gmm_x = np.linspace(-2, 1.5, 5000)
gmm_y = np.exp(gmm.score_samples(gmm_x.reshape(-1, 1)))
# Construct function manually as sum of gaussians
gmm_y_sum = np.full_like(gmm_x, fill_value=0, dtype=np.float32)
for m, c, w in zip(gmm.means_.ravel(), gmm.covariances_.ravel(), gmm.weights_.ravel()):
gauss = gauss_function(x=gmm_x, amp=1, x0=m, sigma=np.sqrt(c))
gmm_y_sum += gauss / np.trapz(gauss, gmm_x) * w
# Make regular histogram
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[8, 5])
ax.hist(samples, bins=50, normed=True, alpha=0.5, color="#0070FF")
ax.plot(gmm_x, gmm_y, color="crimson", lw=4, label="GMM")
ax.plot(gmm_x, gmm_y_sum, color="black", lw=4, label="Gauss_sum", linestyle="dashed")
# Annotate diagram
ax.set_ylabel("Probability density")
ax.set_xlabel("Arbitrary units")
# Make legend
plt.legend()
plt.show()
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