Write a Python program that implements the Naive Bayes classifier.
Your program should take as input a dataset file and a set of training
labels in the format given in the example datasets on the course website.
As output your program should produce predicted labels for the test
dataset which are feature vectors whose labels are not given for training. 

To avoid divide by zero error that will occur when the variance is zero,
we use the pseudocount method. In this method we initialize the mean vector
to be some small value. So instead of initializing the mean to zero
we set it to 0.1 for example. See the document "Naive Bayes variance pseudocount"
in our course google drive.

Submit your assignment by copying it into the directory
/afs/cad/courses/ccs/s20/cs/675/002/<ucid>.
For example if your ucid is abc12 then copy your solution into 
/afs/cad/courses/ccs/s20/cs/675/002/abc12.

Your completed assignment is due by 1pm Feb 10th 2020