Chromatic orbs poe full#
To do this, currently I'm using just the single best point estimate of X, but I intend to change this so it uses the full distribution instead. To do this, we can create some fake data. Previously, I had allowed the model to consider non-integers as well, but in the end I think it makes sense to constrain it and it makes life easier later.Īfter we do our estimation, we want to know what we expect the data to look like. Rounding occurs at all steps for X so that we only end up considering integer values. The initial value of X is randomized, but all for the model are constrained to be maximally 100 and minimally 0. We're doing the learning on all of the data (pre- and post-1.1), there's no train/test set, but I don't think overfitting will be an issue given that there's only a single parameter. Specifically, I'm using a single chain of length 10,000 (with a 500 iteration burnin). So the first step is to come up with an estimate of X, and like I said I'm using MH to do that. That tells us that 12 is the single best estimate, but there's some uncertainty and we can use that later to provide better estimates for what we care about, the probability of rolling the colors we want. So, maybe 20% of the samples had X=11, 60% were X=12, and 20% were X=13. So what we get out of the algorithm is not just a single value of X, but we get a distribution of values. And when you look at the set of values that were chosen, those values will (in the limit) represent the true probability distribution of X, given the data.
Chromatic orbs poe free#
Feel free to google, but basically over time it's going to come up with samples of what it thinks the value of X is. For those not familiar, MH is a Markov Chain Monte Carlo method of estimation. Because my background is in Bayesian methods (and I love when my programs run super slowly), I decided to go with Metropolis-Hastings. Because we can calculate the exact probability of getting any set of colors from the previous set (given a specific value of X), this is a problem which can lend itself well to many parameter estimation techniques. So this is an interesting little problem. Machine learning details (for those interested): I'm assuming X exists between 0 and 100 with uniform prior. Just fyi, I'm planning on calculating X using a simple Metropolis-Hastings algorithm. Otherwise, just add your data and I'll report back once we've got enough that we can run the stats. If you're interested, let me know here to add visibility. So if you want to add data from such an item be sure to post the listed stat requirements rather than the requirements for the base item type.
Chromatic orbs poe mod#
This matters less for items with many sockets, but heavily biases items with fewer sockets and with lower base stat requirements.Ĥ) Obviously don't mess with other people's data.ĥ) I had originally hoped to avoid items with -stat requirement mods, but I seem to recall reading a mod post that made it clear that chromatics would roll off the modified stat values. You need to start recording information before the roll happens, this should avoid cases where people report only "rare" occurrences.ģ) An additional purpose of collecting the previous colors is so that we can run a simulation and take into account the fact that you'll never roll the exact same colors twice. We need all the base item stat requirements, the number of previous colors on the item (sum of each color), and the sum of each color for the rolled item afterward.Ģ) The purpose of collecting the previous colors on the item is to avoid bias in reporting. I'm a very poor self-found character, so I'm just going to note my typical usage.ġ) Note each roll in detail. We need data to confirm the exact number, and it may be the case that this is not how PoE actually instantiates chromes. The nice aspect of this theory is that there is only a single constant that we need to infer. The chance of blue is (200 + X) / (200 + 3X), the chance of green is (X) / (200 + 3X), and the chance of red is the same at (X) / (200 + 3X). So, for this example we have a 200INT item, with let's say one socket to roll. I believe my fellow Redditors that it's more likely that every color has its stat requirement augmented by X. My original conjecture was that a constant X is added to off-colors in order to give them non-zero probability. Now, of course, this exact proposal wouldn't work! A 200INT item would have zero chance of rolling non-blue sockets. A roll then occurs from 1 to the sum of these integers and where that roll fits determines the color of the socket.
Let's call R the integer for red, B for blue and G for green. Every possible option (R, G, or B in this case) is given an integer value.
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