OP is following the same variable names of the article. I prefer that over changing the variable names and then figuring out what variable name maps in code to the article.

>>> ᚨ=3

>>> ᛒ=6

>>> ᚨ+ᛒ

9

Does it still work if you update m as you go?

Best case error is of course zero, but if you look at my output then you will see as I did that the worst case is a very conservative bound (i.e. 15X bigger than what might "tend to happen". That matters a lot for "space usage" since the error =~ 1/sqrt(space) implying you need a lot more space for lower errors. 15^2 = 225X more space. Space optimization is usually well attended for this kind of problem. And, hey, maybe you know something about the input data distribution?

So, in addition to the worst case bound, average case errors under various distributional scenarios would be very interesting. Or even better "measuring as you go" enough distributional meta data to get a tighter error bound. That latter starts to sound like it's one of Knuth's Hard Questions Which if You Solve He'll Sign your PhD Thesis territory, though. Maybe a starting point would be some kind of online entropy(distribution) estimation, perhaps inspired by https://arxiv.org/abs/2105.07408 . And sure, maybe you need to bound the error ahead of time instead of inspecting it at any point in the stream.

In this example, they have the length of the list and choose the threshold to give them a desired margin of error.

  return '⊥'

I also changed the logic from == to >= to trigger unfailingly, and pass in the "window"/threshold to allow my code to work without internal awareness of the length of the iterable:

    from random import random

    def estimate_uniques(iterable, window_size=100):
        p = 1
        seen = set()

        for i in iterable:
            if i not in seen:
                seen.add(i)
            if random() > p:
                seen.remove(i)
            if len(seen) >= window_size:
                seen = {s for s in seen if random() < 0.5}
                p /= 2
        return int(len(seen) / p)

Don't know what they're getting at in the code, though.

That's unfortunate, because in the study of computer programming languages, it means "undefined" (raise an error).

I have no idea what your point is.

  import math
  import random