this post was submitted on 26 Feb 2024
748 points (95.7% liked)
Programmer Humor
19843 readers
151 users here now
Welcome to Programmer Humor!
This is a place where you can post jokes, memes, humor, etc. related to programming!
For sharing awful code theres also Programming Horror.
Rules
- Keep content in english
- No advertisements
- Posts must be related to programming or programmer topics
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Theres plenty of cases where I would like to do some large calculation that can potentially give a NaN at many intermediate steps. I prefer to check for the NaN at the end of the calculation, rather than have a bunch of checks in every intermediate step.
How I handle the failed calculation is rarely dependent on which intermediate step gave a NaN.
This feels like people want to take away a tool that makes development in the engineering world a whole lot easier because "null bad", or because they can't see the use of multiplying 1e27 with 1e-30.
Well, I'm not saying that I want to take tools away. I'm explicitly saying that a
ieee_754_f64
type could exist. I just want it to be named annoyingly, so anyone who doesn't know why they should use it, will avoid it.If you chain a whole bunch of calculations where you don't care for
NaN
, that's also perfectly unproblematic. I just think, it would be helpful to:NaN
check after such a chain of calculations, because it can be a real pain, if you don't do it.NaN
, then you have guarantees that, for example, addition will never produce aNaN
. It allows you to remove some of the defensive checks, you might have felt the need to perform on parameters.Special cases are allowed to exist and shouldn't be made noticeably more annoying. I just want it to not be the default, because it's more dangerous and in the average applications, lots of floats are just passed through, so it would make sense to block
NaN
s right away.What do you do about a dataset which contains 11999 fine numbers, but one of them is NaN because George called in sick that week? Throw away the whole dataset because it doesn't fit the data type?