Natural Language Processing with Classification and Vector Spaces

Course Q&A Natural Language Processing NLP with Classification and Vector Spaces
1

Hi I am trying to do the final lab for week 3 of Natural Language Processing with Classification and Vector Spaces

Assignment: Vector Space Models | Coursera

My problem is with the final question, the compute_pca function

This is my attempt. There is something I am doing wrong about which way round each of the matrices is. The incoming X matrix for the example is of shape (3,10). I am sure that the covariance matrix I need is shape (10, 10). Just calling np.cov(X_demeaned) gives a (3, 3) covariance matrix so I am fairly confident that np.cov(X_demeaned, rowvar=False) is correct. But from then on I don’t have any confidence that the matrices or vectors are the right way round. I have to call eigen_vecs_subset.T to make the final dot product work, but the numbers it produces are wrong, so something somewhere is the wrong way round. I have tried so many different permuations of transposing things, taking columns not rows for subsets, etc, etc, but I haven’t been able to guess it. Can you help? Thank you!

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I instrumented my code for compute_pca to show the dimensions of all the relevant objects and here’s what I see with code that passes the tests:

X.shape (3, 10)
X_demeaned.shape (3, 10)
covariance_matrix.shape (10, 10)
eigen_vals [-7.03941390e-17 -3.60417070e-17 -1.30858621e-17 -8.61317229e-19
  2.07977247e-19  3.78308880e-18  1.81729034e-17  5.06232858e-17
  2.50881048e-01  5.48501886e-01]
idx_sorted [0 1 2 3 4 5 6 7 8 9]
eigen_vecs_subset.shape (10, 2)
X_reduced.shape (3, 2)
Your original matrix was (3, 10) and it became:
[[ 0.43437323  0.49820384]
 [ 0.42077249 -0.50351448]
 [-0.85514571  0.00531064]]

Please compare that to what you are getting and let us know if that sheds any light.

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3

OK yes!

First of all posting the eigen_values helped me find that I was demeaning X with the mean of X, not each row of X with the mean of that row

Secondly the problem was the sort of the eigenvalues, where I had to transpose them, sort the transposed matrix, and transpose back

And now it works, thank you

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