Gradient Descent, Linear Regression and running the algorithm using python

https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/

Go to this link it helps you to understand the overview  gradient descent .

Code for this example can be found here

 from numpy import *  
 # y = mx + b  
 # m is slope, b is y-intercept  
 def compute_error_for_line_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, learningRate):  
   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("data.csv", delimiter=",")  
   learning_rate = 0.0001  
   initial_b = 0 # initial y-intercept guess  
   initial_m = 0 # initial slope guess  
   num_iterations = 1000000  
   print "Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points))  
   print "Running..."  
   [b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)  
   print "After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points))  
 if __name__ == '__main__':  
   run()  

For Running this program you need Python and numpy should be installed.

Install Python : Download (Install Python and set classpath/path)
Install Numpy : Download  (Ater installing python follow the below steps to install Numpy.)

1:  go to this website to download correct package: http://sourceforge.net/projects/numpy/files/  
2:  unzip the package  
3:  go to the unzipped folder   
4:  open CMD here (path of the unzipped foolder)  
5:  then use this command to install numpy: " python setup.py install "  

Note: Installing numpy will take more time like 10mins be patient.

After completing all the above steps , the only left is download the complete program , data.csv file from Github, then go to the download folder in cmd prompt and give command as

1:  python gradient_descent_example.py  

Please check the screen shots .

Gradient Descent, Linear Regression and running the algorithm using python

Installing Numpy