LinearRegression.py

from __future__ import division
import numpy as np 
import matplotlib.pyplot as plt 
import math

data = np.genfromtxt('input.txt',delimiter=',')
x = data[:,0] 
y = data[:,1]
m = y.shape[0]
x = x.reshape((m, 1))
x = np.append(x, np.ones((m, 1)), axis=1)

def cost(x, y, theta=np.zeros((2,1))):
	J = 1/(2*m) * sum((x.dot(theta).flatten()- y)**2)
	return J

def gradient_desc(x, y, theta=np.zeros((2,1)), alpha=.01,iterations=15000):
	J = []
	for numbers in range(iterations):
		a = theta[0][0] - alpha*(1/m)*sum((x.dot(theta).flatten() - y)*x[:,0])
		b = theta[1][0] - alpha*(1/m)*sum((x.dot(theta).flatten() - y)*x[:,1])
		theta[0][0],theta[1][0]=a,b
		if numbers % 1000 == 0:
			print('Iteration: %s Cost: %s' % (numbers, cost(x,y,theta)))
	return theta


theta  = gradient_desc(x, y)
print(theta)