Slide 13
Slide 13 text
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# run PCA
from sklearn.decomposition import PCA
pca = PCA(n_components=n_components,
svd_solver='randomized’,
whiten=True)
pca.fit(X_train)
# reconstruct original space
code = pca.transform(sample.reshape(1, -1))
reconstructed = pca.inverse_transform(code)