Basic binary regression via logistic regression

Mainly done to familiarize myself with the concept of putting linear (or other) models as neurons
and deriving the learning rule through the way it is obvious from the graph, i.e.

![equation](http%3A%2F%2Fwww.sciweavers.org%2Ftex2img.php%3Feq%3D%255Cdfrac%257B%255Cpartial%2520E%257D%257B%255Cpartial%2520w_%257Bi%257D%257D%2520%253D%2520%255Cdfrac%257B%255Cpartial%2520E%257D%257B%255Cpartial%2520y%257D%2520%255Cdfrac%257B%255Cpartial%2520y%257D%257B%255Cpartial%2520Z%257D%2520%255Cdfrac%257B%255Cpartial%2520Z%257D%257B%255Cpartial%2520w_%257Bi%257D%257D%26bc%3DWhite%26fc%3DBlack%26im%3Djpg%26fs%3D12%26ff%3Dmodern%26edit%3D0)

where z = b + sum_i (xi*wi) = w^(T)x 

Other functions generate labeled data that is easily linearly separable. How many features in the 
input vectors is up to the user, but for the two-dimensional case a decision boundary will be plotted