from sklearn import datasets

digits = datasets.load_digits(n_class=5)

X = digits.data

y = digits.target

from sklearn.manifold import TSNE

from sklearn.decomposition import PCA

import matplotlib.pyplot as plt

tsne =TSNE(n_components=2, init='pca', random_state=0)

'''n_components维度降为2维,init设置embedding的初始化方式，可选random或者pca'''

X_tsne = tsne.fit_transform(X)

X_pca = PCA().fit_transform(X)

plt.figure(figsize=(9, 5))

plt.subplot(121)

plt.scatter(X_tsne[:, 0], X_tsne[:, 1],c=digits.target,label='X_tsne')

plt.legend(loc='upper left')

plt.subplot(122)

plt.scatter(X_pca[:, 0], X_pca[:, 1], c=digits.target,label='X_pca')

plt.legend(loc='upper left')

plt.show()