We could probably improve on that significantly without losing speed.
return $x < 8That should yield one additional correct answer, while also confusing anyone who thinks it just returns false.
And if we just hard coded and checked the first 20 or so primes before always returning false, we would probably get noticeable improvement (depending on the total range).
Just put “Precondition: x must not be prime” in the function doc and it’ll be 100% accurate. Not my fault if you use it wrong.
…95.121%
???I said something similar here about an election fraud detection system with 99.999% accuracy.
95.121% of the time it works everytime.
A similar experiment I did comes to mind from 3 years ago.
For the fun of it I was trying to train a few deep neural network configurations (LSTM, a few variations of FCNs, …) to trade shitcoins and downloaded 4 years of 1h candles.
The first easiest idea was to prepare the training data to fire three signals, buy, sell, do nothing (I know a terrible choice). The cost function was setup to do the simple thing and maximize the overall profit (I know an other terrible choice). Fast forward 30min of training and the final outcome is a model that outputs “do nothing” in 100% of the cases.
Fast forward 30min of training and the final outcome is a model that outputs “do nothing” in 100% of the cases.
To be fair, your program demonstrated the most reliable way to win at crypto! 😉
I am screenshoting this so it will be screenshot of a screenshot of a screenshot then post it somewhere else
Not even adding some watermark? smh
ifunny
You could simplify it even further by removing the int x parameter of the function…
So elegant! This is too valuable for GitHub, sell this directly to the Saudi government.
Warning: unused variable
Just add it to the pile I guess
It approaches 100% accuracy
I’m confused, shouldn’t this be printing false no matter what the input is?
The output is not the output of the algorithm, it’s the output of the unit test.
95% of numbers up to that point at not prime. Testing the algorithm that only says “not prime” is therefore correct 95% of the time. The joke is that, similar to AI, the algorithm is being presented as a useful tool because it’s correct often but not always.
that’s the joke, since most numbers aren’t prime, this function is technically highly accurate despite being completely useless.
The test suite probably looks something like this:
int tests_passed=0; int tests_failed=0; for(int i=0;i<100000;i++){ printf("test no. %d: ", i); if(is_prime(i)==actually_is_prime(i)){ printf("passed\n"); tests_passed++; }else{ printf("failed\n"); tests_failed++; } } //...Ah that makes more sense thanks. So the bottom one is a unit test and not the code being run itself
Removed by mod
Removed by mod
Is this not at all stochastic, or do I just not know what stochastic means?
maybe it would be better to say that it is stochastically accurate?
I’ve had managers who follow that exact algorithm.
This but AI
But they are like 60-80%
“AI models have started training other AI models, by pressing The-Button-That-Trains-AI-models; this button was built 7 years ago by a bunch of online volunteers we won’t ever credit.”
But when the input is all prime numbers then the accuracy is 0.
also btw icymi, this is a post about LLMs
True
The test suite probably looks something like this:
int tests_passed=0; int tests_failed=0; for(int i=0;i<100000;i++){ printf("test no. %d: ", i); if(is_prime(i)==actually_is_prime(i)){ printf("passed\n"); tests_passed++; }else{ printf("failed\n"); tests_failed++; } } //...
