Siraj LR-GD.py

from numpy import *



def compute_error_for_given_points(b,m points):
	totalError=0
	for i in range(0,len(points)):
		x= points[i, 0]
		y= points[i,1]
		totalError += (y-(m*x +b))**2
	return totalError / float(len(points))


def step_gradient(b_current,m_current, points,learning_rate):

	b_gradient = 0
	m_gradient = 0
	N= float(len(points))
	for i in range(0, len(points)):
		x = points[i,0]
		y= points[i,1]
		b_gradient += -(2/N) * (y - ((m_current *x) + m_current))
		
		m_gradient += -(2/N) *x* (y - ((m_current *x) + m_current))

	new_b=b_current - (learning_rate * b_gradient)
	new_m=m_current - (learning_rate * m_gradient)
    return [new_b,new_m]


def gradient_descent_runner(points, starting_b,starting_m,learning_rate,num_iterations):
	b=starting_m
	m=starting_m

	for i in range(num_iterations):
		b,m=step_gradient(b,m,array(points),learning_rate)
	return [b,m]

def run():
	points=genfromtxt('data.csv', delimiter=',')
	learning_rate=0.0001
	intial_b=0
	initial_m=0
	num_iterations=1000
	[b,m]= gradient_descent_runner(points, intial_b,initial_m,learning_rate,num_iterations
	print(b)
	print(m)





if __name__=='__main__':
	run()