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()
