So in Optional Lab: Multiple Variable Linear Regression (Lab02, week02)

There’s this cost function implemented for a given dataset. Accordingly when I execute the kernel cell i get:

Cost at optimal w : 1.5578904045996674e-12

Whereas when i just create a python file and write the same function with the same initial and training values the output I am getting is this:

Cost at optimal w : 2.0487331486181772

Not sure what’s wrong.

This is the code that I wrote

```
x_train = np.array([[2104, 5, 1, 45], [1416, 3, 2, 40], [852, 2, 1, 35]])
y_train = np.array([460, 232, 178])
m = x_train.shape[0]
n = x_train.shape[1]
b_init = 785.1811367994083
w_init = np.array([ 0.39, 18.75, -53.36, -26.42])
def compute_cost(X, y, w, b):
m = X.shape[0]
cost = 0.0
for i in range(m):
f_wb_i = np.dot(X[i], w) + b
cost = cost + (f_wb_i - y[i])**2
cost = cost / (2 * m)
return cost
cost = compute_cost(x_train, y_train, w_init, b_init)
print(f'Cost at optimal w : {cost}')
```