步骤如下：
示例
from sklearn.manifold import TSNE
from sklearn.datasets import load_iris, load_digits
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
digits = load_digits()
X_tsne = TSNE(n_components=2, random_state=33).fit_transform(digits.data)
X_pca = PCA(n_components=2).fit_transform(digits.data)
font = {"color":"darkred", "size": 13, "family" : "serif"}
plt.style.use("dark_background")
plt.figure(figsize=(8.5, 4))
plt.subplot(1,2,1)
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=digits.target, alpha=0.6,
cmap=plt.cm.get_cmap("rainbow", 10))
plt.title("t-SNE", fontdict=font)
cbar = plt.colorbar(ticks=range(10))
cbar.set_label(label="digit value", fontdict=font)
plt.clim(-0.5, 9.5)
plt.subplot(1,2,2)
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=digits.target, alpha=0.6,
cmap=plt.cm.get_cmap("rainbow", 10))
plt.title("PCA", fontdict=font)
cbar = plt.colorbar(ticks=range(10))
cbar.set_label(label="digit value", fontdict=font)
plt.clim(-0.5, 9.5)
plt.tight_layout()
plt.show()