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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def plot(x,y,m,b): | |
| plt.scatter(x,y) | |
| # datapoints | |
| y_pred=[i*m+b for i in x] | |
| plt.plot(x,y) #classifier line | |
| plt.show() | |
| def grad_desc(x,y): | |
| m_curr=b_curr=0 #starting value m_curr,b_curr | |
| iterations=1000 #Total iteration count | |
| learning_rate=0.001 #Assumed learning rate | |
| n=len(x) #length of x vector | |
| for i in range(iterations): | |
| y_pred = m_curr*x + b_curr #predicted value of y | |
| md=(-2/n)*sum(x*(y-y_pred)) #value of derivative of cost w.r.t m | |
| cost=sum((y-y_pred)**2)/n | |
| bd=(-2/n)*sum(y-y_pred) #value of derivative of cost w.r.t b | |
| m_curr=m_curr-learning_rate*md | |
| b_curr=b_curr-learning_rate*bd | |
| print('m {} b {} cost {}'.format(m_curr,b_curr,cost)) | |
| plot(x,y,m_curr,b_curr) | |
| if __name__=="__main__": | |
| x=np.array([2,3,4,5,6,7,8,9,11,10,12]) | |
| y=np.array([7,10,13,16,19,22,25,28,34,31,37]) | |
| grad_desc(x,y) |
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