For a project, I want to compare the runtime of different median finding algorithms. I started with the "Medians of Medians" and basically used the code I found by Geeks for Geeks.
I tested it by comparing to the standard python method of calculating the median.
if __name__ == '__main__':
arr = random.sample(range(1, 10000000000), 10000001)
arr1=arr[:] # i copied the list to make sure they both have the same starting position
t1=time.time()
print("std median", statistics.median(arr))
t2 = time.time()
print("time std median:",t2-t1)
t12 = time.time()
n = len(arr1)
k = n // 2 + 1 #median for odd number of elements
print("Med of Med:", kthSmallest(arr1, 0, n - 1, k))
t21 = time.time()
print("time med of med:", t21-t12)
For an unknown reason my runtimes are way to high and just wrong. Finding the median in an array of ~10 Mio elements took the following time:
Standard Python method: 13.28 seconds
My implementation of median of medians: 28.91 seconds
Is there something wrong with the implementation I found on Geek for Geeks? It should be the other way around. The standard Python method has a runtime of O(n log n) and Median of Medians runs in O(n), so it should be faster!
Does anyone know what I did wrong and could give me a hint how to fix it?
