Chapter 9 - Clustering for Semi-Supervised Learning

I get a different set of representative digits from those in the notebook. Labelling with

y_representative_digits = np.array([
0, 1, 3, 2, 7, 6, 4, 6, 9, 5,
1, 2, 9, 5, 2, 7, 8, 1, 8, 6,
3, 1, 5, 4, 5, 4, 0, 3, 2, 6,
1, 7, 7, 9, 1, 8, 6, 5, 4, 8,
5, 3, 3, 6, 7, 9, 7, 8, 4, 9])

produces a log_reg score of 92.4%. Alternatively using:

y_representative_digits = y_train[representative_digit_idx]

Start in the Chapter 9 notebook and inspect how representative_digit_idx, y_train, and y_representative_digits are produced. Compare the notebook's representative digits and logistic-regression score with the reported values; done means the discrepancy is explained and the notebook's expected result is reproducible or clearly documented.

Written by the indexing model from the issue text.