I've uploaded the files I used for HW2 problem 3 D & E here:
There are seven files in the archive: smo_test, smo_train, svm_test, and svm_train were explained in the HW question. j_rand_not_i and clip_alpha are just helper functions from the SMO code to make it cleaner. Beware: svm_test, and svm_train are slightly modified from the class version (see the first few lines) and I will discuss that below.
If you run smo_verify and get a plot like the one below, then your SMO code works, and you just need to characterize it's performance.
Once you have smo_verify correct you can try out svm_test, first you need to run svm_train to get w & b.
There a two parameters to play with to see how the algo performs on different data sets; max_passes and tol. Some of the iterations can take up to 10min, so it's worth while to automate the testing. I did a crude version of this in the attached file: char_svm. In order for char_svm to call the train and test scripts I had to make them into functions. This was a very small change and only the first line was altered.
With max_passes set to 100 and training on the 1400 samples I was able to achieve a test accuracy of 0.005 or 0.5%. That is amazing performance!
The simplified smo algo has a random component so you won't get the same results every time, so it's a good idea to take multiple samples, and average out the results. Below is one example where I took three samples.
If you have any questions, feel free to contact me.