Principal Component Analysis doesn't work

Question

I'm trying to do PCA with pretty simple dataset, but I'm still getting this error: AttributeError: 'PCA' object has no attribute 'singular_values_'

Here is code:

import numpy as np
from sklearn.decomposition import PCA
X = np.array([[0.92, 0.51], [0.72, 0.59],
              [0.83, 1.03], [0.81, 1.21],
              [0.82, 0.63], [0.93, 0.68],
              [0.84, 0.57], [0.89, 1.52],
              [0.89, 1.04], [0.95, 0.99]])
pca = PCA(n_components=2)
pca.fit_transform(X)
print(pca.mean_)
print(pca.components_)
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)
print(pca.singular_values_)
print(pca.n_components_)
print(pca.noise_variance_)

I get everything except for singular_values_

Thank you for your help!

Answer 1

The singular_values_ attribute was added in sklearn 0.19, released in Aug-2017. That you cannot access it indicates you are using an older version.