liminal @liminal - 6mo
Actually curious about the reasoning for it π We care about some sort of threshold classification, with some sort of "pending"/leniancy state for newcomers. Maybe even a sinkhole point-of-no-return-make-a-new-npub-bitch for spam/noted toxic individuals given your network. A sigmoid is standard practice for classification, but asymptotic bounds of 0 and 1 don't really help those that have been in the game for a while. So we want to classify yes and no, in between state, and also note the very (un)trustworthy individuals. Tan and cubic curves accomplish that. They grow very fast after a certian point. With minimal assumptions, you can just encode every data point as Β±1 for positive/negative interactions, and put the average * scaling constant into the function. Or you can weight the interactions by type (mutes>follows> sentiment classification of comments > reaction classification, or weight based on if the points are coming from your follows) and compute the weighted average. https://i.nostr.build/LeV86.jpg https://i.nostr.build/4oG23.jpg https://i.nostr.build/VwVnj.jpg Coracle's WOT formula, where mutes are the argument and follows are the parameter.
Feel like i didnt understand math till Calc 3 π€£π€£π π°ππ even more so when i took Dynamic Systems
liminal @liminal - 4mo
Curious about the formula used to calculate π
Quite curious at the reasoning or background for the formula vs others. Little brainstorm about using some formulas in this thread. What i'd like out of a WOT is 1) newcomers get leniency, so small number of interactions/ small score is okay 2) below a certian level i dont care, above a certian level i dont care either Think the cubic seems to accomplish that. Forget the mention of tan in this thread though π nostr:nevent1qqs27cam2wcnc8gvr4alja47fm8gf8xvcx3gn4lczj5hhcg9f8f900gpzemhxue69uhhyetvv9ujumn0wd68ytnzv9hxgq3qm3xdppkd0njmrqe2ma8a6ys39zvgp5k8u22mev8xsnqp4nh80srqxpqqqqqqzd2jzu6