FindItemsForQuota
Finds the items to min-max the quota and tps them in front of the company shelf.
Last updated | 7 months ago |
Total downloads | 2890 |
Total rating | 1 |
Categories | Mods BepInEx |
Dependency string | Rbmukthegreat-FindItemsForQuota-1.2.3 |
Dependants | 0 other packages depend on this package |
This mod requires the following mods to function
BepInEx-BepInExPack
BepInEx pack for Mono Unity games. Preconfigured and ready to use.
Preferred version: 5.4.2100README
FindItemsForQuota
A mod that finds the items that sum as close as possible to quota (a.k.a "min-maxing") and teleports them in front of where they need to be sold.
Usage
To use this mod, you need to be at the company, wait for the ship to be landed (wait until the lever stops saying "[wait for ship to land]"), and then type one of the following commands into the in-game chat:
".find moon" where "moon" is either rend or art
".find target"
.find rend will find the amount of money needed to either hit quota or go to rend (have 550), .find art does something similar, and .find target will find the amount of money you need to have an extra target amount of money after overtime has been calculated.
For example, say I wanted to buy a jetpack, and I currently have $200 saved up. Since jetpacks cost $500, I would then run /find 500, and it would find the items so that you have $700 after the overtime bonus.
Issues
I've recently discovered that if there are too many items on ship (> 250) then a wide number of desyncs happen, including shotguns having the safety on/off for different people, and for this mod in particular, the value of scrap on ship showing up as lower for people who are not host. This in particular means that the mod will grab too many items because it does not have the correct value of scrap on the ship. In particular, this means that
If you are not host, do not use this mod if there are > 250 items on ship.
Since this host cannot desync from themselves, this mod has no issues when you are the host. Of course, if there are <=250 items, then any person (not just the host) can use this mod freely, without any problems.
Installation
- Install BepInEx
- Find the location of your game by clicking on the gear and selecting Manage then browse local files
- Extract the mod to BepInEx/plugins
Or let thunderstore handle it.
References
A lot of the code for this mod has been inspried by ShipLoot. This project would not have been completed without the help of it's source code.
For anyone interested, the problem this mod has to solve is something called the subset-sum problem, defined as follows:
Given a list of numbers $a_0, \ldots, a_n$, and a target $T$, return a sublist of the numbers $a_{k_0}, \ldots, a_{k_m}$ with $$\sum_{i=0}^{m} a_{k_i} = T.$$
In the context of lethal company the $T$ is the quota and then $a_i$ are the scrap value of items on ship.
In complexity theory, there are two complexity classes that the entire world is thinking about: P and NP. P, short for polynomial, is the class of algorithms that can be solved in polynomial (i.e., efficient) time. NP stands for non-deterministic polynomial, meaning algorithms that can be verified (i.e., given the output of the algorithm, you can check it's correctness) in polynomial time. The hardest and most important problem from theoretical computer science is the question of whether or not P = NP. It is widely believed that P $\neq$ NP, but no one has been able to prove that so far (and if someone could, they would win every award in computer science, and $1,000,000!). There is a class of problems called NP-hard, which, if ANY of them were also in P, then P = NP. In particular, subset-sum (or more generally the knapsack problem) is NP-hard. NP-hard also means that the most efficient algorithm would take $10^{37}$ years to complete on an input size of size 100, which is an obvious problem since in lethal company you can have upwards of 300 items on ship.
The only thing one can do in these sort of situations is look for good approximation algorithms, that use randomness to approximate the result with high probability. The paper I found detailing such an algorithm was Przydatek99, which gives a shockingly low relative error bound with extremely high probability. This project would also not have been completed without the incredible results of this paper, bringing the time the algorithm runs down from $10^{37}$ years to more like $0.5$ seconds.