LogisticRegression.py

#Not mine code, but a very good example that helped me understand Logistic Regression
#Found it here: http://stackoverflow.com/questions/25880634/logistic-regression-objects-are-not-aligned

import numpy as np
from scipy.optimize import fmin_bfgs
import io
data = np.loadtxt('ex2data1.txt',delimiter=",")
m,n = data.shape
X = np.array(np.column_stack((np.ones(m),data[:,:-1])))
y = np.array(data[:,2].reshape(m,1))
theta = np.array(np.zeros(n).reshape(n,1))

def sigmoid(z):
    return 1/(1+np.exp(-z))

def hypothesis(X,theta):
    return sigmoid( X.dot(theta) )

def cost(theta):
    h = hypothesis(X,theta)
    cost = (-y.T.dot(np.log(h))-(1-y).T.dot(np.log(1-h)))/m
    r = cost[0]
    if np.isnan(r):
        return np.inf
    return r

def gradient(theta):
    theta = theta.reshape(-1, 1)
    h = hypothesis(X,theta)
    grad = ((h-y).T.dot(X)).T/m
    return grad.flatten()

def fmin():
    initial_theta=np.zeros(n)
    theta=fmin_bfgs(cost,initial_theta,fprime=gradient)
    return theta

theta = fmin()