import numpy as np
import matplotlib.pyplot as plt
from time import time
from sklearn.manifold import TSNE
def plot_embedding(data, label, title):
ids = np.unique(label)
label_color = label.copy()
for i, label_id in enumerate(ids):
label_color[label_color==label_id] = i
x_min, x_max = np.min(data, 0), np.max(data, 0)
data = (data - x_min) / (x_max - x_min)
fig = plt.figure()
ax = plt.subplot(111)
for i in range(data.shape[0]):
plt.text(data[i, 0], data[i, 1], str(label[i]),
color=plt.cm.Set1(label_color[i] / 10.),
fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.title(title)
plt.show()
return fig
def sne(data, label):
print('Computing t-SNE embedding')
tsne = TSNE(n_components=2, init='pca', random_state=0)
t0 = time()
result = tsne.fit_transform(data)
fig = plot_embedding(result, label,
't-SNE embedding of the digits (time %.2fs)'
% (time() - t0))
