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10 lines
424 B
10 lines
424 B
import numpy as np |
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import matplotlib.pyplot as plt |
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from sklearn.manifold import TSNE |
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data = np.random.rand(64, 10) # 64个样本，每个样本维度为10 |
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target = np.arange(2).repeat(32) # 生成64个标签，用于区分样本目标 |
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t_sne_features = TSNE(n_components=2, learning_rate='auto', init='pca').fit_transform(data) |
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plt.scatter(x=t_sne_features[:, 0], y=t_sne_features[:, 1], c=target, cmap='jet') |
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plt.show() |
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