sklearn.manifold. TSNE

from sklearn.manifold import TSNE
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits

mnist=load_digits()
tsne =TSNE()

mnist_tsne=tsne.fit_transform(mnist.data)

# 상대적인 거리를 이용해 가까운 것들은 더 가깝게, 먼 것들은 더 멀게 만든다.

 

xmax, ymax = mnist_tsne.max(axis=0)
xmin, ymin = mnist_tsne.min(axis=0)

colors = ["#476A2A", "#7851B8", "#BD3430", "#4A2D4E", "#875525",
          "#A83683", "#4E655E", "#853541", "#3A3120", "#535D8E"]

plt.figure(figsize=[14,14])
plt.xlim([xmin,xmax])
plt.ylim([ymin,ymax])
for i in range(mnist.data.shape[0]):
    plt.text(mnist_tsne[i,0], mnist_tsne[i,1],str(mnist.target[i]),
             color=colors[mnist.target[i]], fontdict={'weight':'bold', 'size':9})

before

after(TSNE)