import numpy as np

m=20
X=np.array((np.arange(0,10,0.5)))
y=2*X+0.5
X=np.concatenate((X.reshape(m,1),np.ones(m).reshape(m,1)),axis=1)
randn=np.random.rand(len(X))
y=y-randn
y=y.reshape(m,1)
#X.shape=(20,2),y.shape=(20,1)
theta=np.ones(2).reshape(2,1)

a=0.01
epochs=100
def linear(x,theta):
    return np.dot(x,theta)

def gradient(y,ypred,x):
    return (ypred-y)*(x.reshape(2,1))#The Question

for i in range(2):
    for j,x in enumerate(X):
        ypred=linear(x,theta)
        theta=theta-a*gradient(y[j],ypred,x)

print(theta)