Over the holidays, I was working on implementing my algorithm into my code, since most subroutines were closely linked and used large amounts of data, it was difficult to test until it was all done. So I wrote all the code and then tested, and bug fixed, and once it was working as intended, I realised that the computer can't handle the amount of data I was pulling through (200 million numbers and growing exponentially). Which is why I needed a moderating function to be the genome attribution that conveys inputs to output rather than an output for every input. This would mean much fewer data and much more math, specifically massive matrice math which I don't have a strong enough knowledge on. Luckily there is a library called NEAT-python that does a lot of it for me, so I'll be trying to use their functions to better implement my neural network, which means staying away from my algorithm a bit, but otherwise, it won't work.