Post by Shimmr in Course of Temptation comments

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Just had some fun digging through the code (learning new thing along the way) and I think I see where the problem is. The variable names are a bit confusing. The only originally normal distributed value here is the random number from the bm rng algorithm (standard normal distribution to be exact). "stddev", as it is in the code, is really just the the distance from the middle/mean of the range to its edge.

The idea seems to be to create a normal distribution of stat values by multiplying "stddev" with the bm rng value. Since the mean of this distribution is at 0 you then just have to offset it by the mean value.

This kinda happens in the random_bm function already, but "mean" and "stddev" were dived by 1000 beforehand (creating a random factor for the the rangesize i presume). This makes the calculations in rir_bm redundant. The problem is that this distribution does not fit within the specified range. A solution to this is to just divide the bm rng value by 2 (or up to 2.7) to squish the distribution and then either only accept values between -1 and 1 or just clamp the rest like before. Create a kinda normalized factor so to speak.

To summarize my solution would be: Don't divide "mean" and "stddev" by 1000, divide the original "num" value in random_bm by 2 (up to 2.7) and create a (kinda) normalized factor to multiply "stddev" with and offset it by adding "mean".

pornbrainer105 days ago

What do you mean by “bm rng”? Do you mean the random_bm() function or something else? When I checked how the random_bm() algorithm works by recalculating it myself, it seemed to produce results accurate to the stddev input, but I could be wrong. I could check again but I don’t see the reason for altering the algorithm in random_bm(), it’s only the use of rir_bm() that’s the problem. What’s your reasoning for how random_bm’s stddev is only the distance from the middle to the edge of the range? I didn’t get that impression. Unless I’m just confused about the term “bm rng”.

Shimmr105 days ago

By "bm rng" I mean the Method used to calculate the random number. It's called "Box–Muller transform", I just forgot the name and shortened it.

I called stddev that is because it is the same calculation. It is halve the rangesize and thus the distance between mean and min/max of the range.

The way the final stat value is calculated is weird or I might be misunderstanding something. Both stddev and mean are divided by 1000 and thus normalized to the max range (0=0 ... 1000=1). rir_bm() calls random_bm() with stddev and mean and returns a factor which is then multiplied by the rangesize and adds that to the start value. The problem is the multiplication with the rangesize.

If you look at the distribution of the values random_bm() returns, you get a normal distribution. The thing is that all these values are normalized to the max range of 1000. If you simply multiply them by 1000 you get a normal distribution of stat values centered correctly around mean, which I thought was what we are trying to calculate here.

The important part is that the the values random_bm() returns are absolute values(with a root of 0), but rangesize is relative. It's a bit difficult for me to explain, but basically this rangesize can be anywhere on the range. Similar to your example, if you take values on the higher end 800 to 900 for example. The mean is 850, but if you calculate it with the code you get num=0(mean of the box-muller transform)*stddev+mean=0.85; diff=rangesize*num=100*0.85=85; value=start+diff=800+85=885. And well 850=/=885.

And since both rir_bm and random_bm are basically trying to calculate the same thing in two different ways, I thought just make it simple and use the method I described.

itch.io

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