LR_NP_1.py

## Linear Regression 
import numpy as np

n = 700
alpha = 0.0001

a_0 = np.zeros((n,1))
a_1 = np.zeros((n,1))

epochs = 0
while(epochs < 1000):
    y = a_0 + a_1 * x_train
    error = y - y_train
    mean_sq_er = np.sum(error**2)
    mean_sq_er = mean_sq_er/n
    a_0 = a_0 - alpha * 2 * np.sum(error)/n 
    a_1 = a_1 - alpha * 2 * np.sum(error * x_train)/n
    epochs += 1
    if(epochs%10 == 0):
        print(mean_sq_er)

I think your work is very good, but somehow i am messing it up. When I try to run the code the error goes toward infinity instead of toward zero. This leads to a runtime overflow error and all values in error and y becomes NaN.

Any ideas what could cause this?

I think your work is very good, but somehow i am messing it up. When I try to run the code the error goes toward infinity instead of toward zero. This leads to a runtime overflow error and all values in error and y becomes NaN.

Any ideas what could cause this?

Hi @OregonMortenson add these lines in respective locations
y_train = y_train.reshape(-1,1)

a_0 = a_0[0:300]
a_1= a_1[0:300]

it will work