in Reggie Linear Regression

(https://www.codecademy.com/paths/data-science/tracks/dspath-python-unit-project/modules/dspath-brute-force-lr/informationals/pwp-linear-regression)

Wouldn’t infinite remain the smallest throughout the loops?

datapoints = [(1, 2), (2, 0), (3, 4), (4, 4), (5, 3)]

smallest_error = float(“inf”)

best_m = 0

best_b = 0

for m in possible_ms:

for b in possible_bs:

error = calculate_all_error(m, b, datapoints)

if error < smallest_error:

best_m = m

best_b = b

smallest_error = error

print(best_m, best_b, smallest_error)

I don’t understand why we have smallest_error = float(“inf”)

it seems to me that logically if infinity is smaller than anything else, it would never be replaced by any other “smallest_error”

can someone explain?