When computing the PCA, I always got complexed-value eigenvectors and the direction of the eigenvector is reversed so i cannot pass the test. Can anyone help? Thanks in advanced. below is (pseudo?) code I used to compute eigenvectors for data X calculate meaned data by X_mean = X - X.mean(axis = …

To close the loop on the public thread, everything was correct except one key line of code: when you sort the eigenvectors, remember that they are the columns of the eigen_vecs matrix, not the rows. Check the documentation for np.linalg.eigh to confirm!

C1_W3 assignment compute_pca got complexed-value eigenvectors

Course Q&A Natural Language Processing NLP with Classification and Vector Spaces

paulinpaloalto April 21, 2024, 3:12am 18

This has already been solved on a parallel thread.

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C1_W3 assignment compute_pca got complexed-value eigenvectors

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