import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets as datasets

digits = datasets.load_digits()
print(digits)

#探索数据结构
data = digits.data
label = digits.target
print(data.shape)

n_sample,n_features = data.shape
#t-SNE降维
from sklearn.manifold import TSNE
tsne = TSNE(n_components = 2,init = "pca",random_state = 0)

X_tsne = tsne.fit_transform(data)

x_min,x_max = np.min(X_tsne,0),np.max(X_tsne,0)

X_tsne = (X_tsne - x_min) / (x_max - x_min)

plt.figure(figsize = (12,12))
for i in range(data.shape[0]):
    plt.text(X_tsne[i,0],X_tsne[i,1],str(label[i]),color = plt.cm.Set1(label[i] / 10))
plt.xticks([])
plt.yticks([])
plt.show()