Write a Python program that will take data and trainlabels as input (in
the same format as for previous assignments) as well as a number k. Your
program will output the data with the top k ranked Pearson correlation
coefficient features.  You may use the Python scikit-learn library for 
ranking features by Pearson correlation.

Once this is done then modify run_on_all_data.py to run nearest means,
naive bayes, and linear SVM on the top 10 ranked Pearson correlation 
features for each training/test pair. Remember to use just the training
data to rank features.

Submit your assignment by copying it into the directory
/afs/cad/courses/bnfo/f18/bnfo/615/001/<ucid>
For example if your ucid is abc12 then copy your solution into 
/afs/cad/courses/bnfo/f18/bnfo/615/001/abc12

Submit a hardcopy in class as well.

Your completed assignment is due on November 3rd 2018