import numpy as np
            import numpy.linalg as linalg
            import pandas as pd
            import csv as csv

            # Read in data to a pandas dataFrame. This file has 3 columns, 'x1', 'x2' and 'y'
            data = pd.read_csv(r'.logistic_xy.csv')

            # Convert to a numpy array
            data = np.array(data)

            # Define data matricies

            X = np.array(data[0::,0:3])
            y = np.array(data[0::,3])
            y = y.reshape((99,1))
            W = np.zeros([99,99],dtype=float)
            p = np.zeros([99,1],dtype=float)
            z = np.zeros([99,1], dtype=float)

            # Define hypothesis function
            def hTheta(x):
                g = np.exp(x)/(1+np.exp(x))
                return g

            # Define convergence variable and initialize theta

            theta = np.zeros([3,1],dtype=float)
            thetaNew = theta
            conv = 1

            while conv > 0.01:
                for i in xrange(0,int(data[0::,0].sum())):
                    theta = thetaNew
                    x = np.dot(np.transpose(theta),data[i,0:3])[0]
                    h = hTheta(x)
                    W[i,i] = h*(1-h)
                    p[i] = h

                z = np.add(np.dot(X,theta),np.dot(np.linalg.inv(W),np.subtract(y,p)))
                A = np.linalg.inv(np.dot(X.transpose(),np.dot(W,X)))
                B = np.dot(X.transpose(),np.dot(W,z))

                thetaNew = np.dot(A,B)
                conv = np.linalg.norm(np.subtract(thetaNew,theta))