Write a Python program that implements gradient descent for minimizing
the least squares loss. As a stopping condition check for the objective
between the current and previous iteration. If the objective improves
by less than theta then you stop. The input and output should be the same 
as for nearest means and Naive-Bayes. 

Test your program with the input data

0 0
0 1
1 0
1 1
10 10
10 11
11 10
11 11

and labels 

0 0
0 1
0 2
0 3
1 4
1 5
1 6
1 7

Use eta=.001 and stopping condition of .001. 

Your final w would be close to

w = 0.0889184232356005 0.0907934047968894 

and distance of plane to origin would be about

abs(w0/||w||) = 7.09045903042441

If you change the stopping condition to 0, in other words 
full convergence, then your final w would be

w = 0.0995024069539168 0.0995025677420564 

and distance to origin 

abs(w0/||w||) = 7.77817457926694

Your assignment is due Oct 1st 2018.

Submit your assignment by copying it into the directory
/afs/cad/courses/ccs/f18/cs/675/001/<ucid>.
For example if your ucid is abc12 then copy your Python program into 
/afs/cad/courses/ccs/f18/cs/675/001/abc12.

Usman