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import numpy as np | |
np.random.seed(4) | |
m = 60 | |
w1, w2 = 0.1, 0.3 | |
noise = 0.1 | |
angles = np.random.rand(m) * 3 * np.pi / 2 - 0.5 | |
X = np.empty((m, 3)) | |
from sklearn.decomposition import PCA | |
pca = PCA(n_components = 2) | |
X2D = pca.fit_transform(X) | |
print(X2D) |
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