GradientDescent.py

from numpy import *



def compute_error_for_given_points(b, m, points):
    # sum of squared errors
    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, learningRate):
    # gradient descent
    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) + b_current))
        m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current))
    new_b = b_current - (learningRate * b_gradient)
    new_m = m_current - (learningRate * m_gradient)
    return [new_b, new_m]
        
        
    

def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):

    b = starting_b
    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('https://raw.githubusercontent.com/llSourcell/linear_regression_live/master/data.csv', delimiter=',')
    #hyper parameter
    learning_rate = 0.0001
    # y = mx+b   (slope formula)
    initial_b = 0
    initial_m = 0
    num_iterations = 1000   #small dataset
    [b,m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
    print(b)
    print(m)
    print(compute_error_for_given_points(b, m, points))



if __name__ == '__main__':
    run()