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Warnings running codes in assignment 8:

/lib/python3.6/site-packages/ipykernel_launcher.py:26: RuntimeWarning: invalid value encountered in long_scalars

Training result discrepancies in Assignment 8 Part 2 Recommender Systems. X, Theta, and [email protected] have small differences compared to results in MATLAB.

This is in assignment3 section 1.4 function oneVsAll()

result = minimize(lrCostFucntion, theta0, args=(X, ylabel, lmd), method='TNC', jac=True, options={'disp': True, 'maxiter':1000})

Method='CG' should work, too, or even faster. But there's error.

Suspect a bug inside minimize function, it seems to change the shape of theta in the process, causing dimension issue when trying to do matrix multiplication.

In assignment 6 SVM, section 2.4, the top predictors I got are not exactly the same as in MATLAB codes from class; also in section 2.5, my classifier marks 'emailSample1' as spam, but MATLAB does not; section 2.3, the accuracies are not exactly same, either.

Suspicions are:

• The small differences on the email pre-processing step?
• The kernel used for SVM learning?

If using scipy.misc.imread the png, it will return RGB values in the range of 0-255, and if you use this data matrix for learning and then plot, the image looks weird and changes a lot every time you run it again.

Need feature scaling?

See assignment 4-zero indexing.ipynb:

1. There's warning when running minimization function, but the result seems to be OK;
2. Prediction accuracy is 100%!! Too good to be true.