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Thursday, October 31st, 2024
| Time | Event |
| 12:29a | Symta is too difficult even for me So as you know already I have to generate a lot of unique characters procedurally. And any generation involves some distribution. So I pulled out of my ass the following statistics: 70% of ladies have long hair 25% have mid hair 5% have short hair 70% of men have short hair 20% have mid hair 5% have long hair 5% are fully bald In game these are defined as a list MaleHairLength: 70,short 20,mid 5,long 5,bald These allow me to settle on the hair length of a character. I had some code to pick a random sample from such a distribution. MaleHairLength.find(s~+=?0; p~^(Me{?0}.z*)<<s~).1 That is cryptic and it does really cryptic things inside the find's lambda and (implicitly) outside of it. Now the problem is that I already forgot the implcit variables semantics I introduced years ago. Since most of the code is not command line oneliners or math shit. What is worse, I have never documented it. So now I had to go over the macros implementing these constructs deciphering them. And obviously these macros are not simple. TLDR: I need to document at least the harder parts. Current Mood:  contemplativeCurrent Music: Anvil of Dawn OST |
| 12:17p | ChatGPT ![[info]](http://lj.rossia.org/img/userinfo.gif) necax claims there is a shorter algorithm to sample from a population, but ChatGPT comes with exactly the same algorithm I invented, which it calls "cumulative summing method":https://chatgpt.com/share/67236748-d It is completely equivalent to my list.outcome = $find(s~+=?0; p~^(Me{?0}.z*)<<s~).1method, except that it produces a list of K samples, instead of just one. import random def custom_choices(population, weights, k=1): cumulative_weights = [] total = 0 # Build cumulative weights for weight in weights: total += weight cumulative_weights.append(total) # Generate samples samples = [] for _ in range(k): rand = random.uniform(0, total) for item, cum_weight in zip(population, cumulative_weights): if rand < cum_weight: samples.append(item) break return samples # Test it with your distribution population = ['short', 'mid', 'long', 'bald'] weights = [70, 20, 5, 5] samples = custom_choices(population, weights, k=10) print(samples) Current Mood: contemplative |
| 1:00p | Cops came again to harrass me Currently ACABs impute me prostitution. Last time they claimed I boiled a cat. Before that they insisted I have a firearm. And then that I'm a racist and a russophobe, That I vandalize property and disturb order. These transphones make up a reason of every type. It is easy to harass trans folk. Instead of going after actual criminals. Who have real guns, not imaginary ones. And if they want a whore, they should arrest their moms. But I guess this time municipality unleashed them. Since I filled multiple complaints against the municipality, which impedes my inburgering. Current Mood:  annoyedCurrent Music: Howard Drossin - Sendai Enclave |
| 6:32p |
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