Long For Loop Excecution Time

I am attempting to compare the numerical output of a MATLAB script to that of numpy.

The task, from a homework assignment, is to add the value of 10^-N to itself 10^N times.

The goal is to demonstrate that computers have limitations that will compound in such a calculation. The result should always be 1, but as N increases, the error will also increase. For this problem, N should be 0, 1, 2, …, 8.

My MATLAB script runs quickly with no issues:

solns = zeros(1, 9);

for N = 0:8
    for ii = 1:(10^N)
        solns(N+1) = solns(N+1) + 10^-N;
    end
end

However, using Python 3.8.12 on an Anaconda installation with numpy, this code will not terminate:

import numpy as np

solns = np.zeros((9, 1))

for N in range(len(solns)):
    for _ in range(10**N):
        solns[N] += (10**-N)

Is there an error in the Python code or is there some significance difference between the languages that creates this result.

EDIT:

I let the python code run for awhile and it eventually terminated after 230 seconds. The whole array printed out as just [1., 1., 1., etc.].

>Solution :

You’re rather abusing NumPy there. Especially the += with an array element seems to take almost all of the time.

This is how I’d do it, takes about 0.5 seconds:

from itertools import repeat

for N in range(9):
    print(sum(repeat(10**-N, 10**N)))

Output:

1
0.9999999999999999
1.0000000000000007
1.0000000000000007
0.9999999999999062
0.9999999999980838
1.000000000007918
0.99999999975017
1.0000000022898672

Try it online!