| use scraper::{Html, Selector}; | |
| use std::collections::HashMap; | |
| fn main() { | |
| let source_sites: &[&[&str]] = &[ | |
| &[ | |
| "https://tvtropes.org/pmwiki/pmwiki.php/BreakingBad/TropesAToB", | |
| "https://tvtropes.org/pmwiki/pmwiki.php/BreakingBad/TropesCToD", | |
| "https://tvtropes.org/pmwiki/pmwiki.php/BreakingBad/TropesEToL", |
| /// Const evaluatable Rust implementation of Vose's Alias Method, as described | |
| /// by Keith Schwarz at https://www.keithschwarz.com/darts-dice-coins/ | |
| /// | |
| /// In brief, this is an O(n) precomputation, which allows sampling an arbitrary | |
| /// finite probability distribution in O(1) time, by first simulating a fair | |
| /// n-sided die, followed by a biased coin. | |
| /// | |
| /// Because floating point arithmetic cannot be used in const functions, this is | |
| /// built to operate on integer weights, rather than precomputed probabilities. | |
| /// |
| // Calculate the area of a generalized ellipse (abs(x/a)^n + abs(y/b)^n = 1). | |
| // I'm considering this as the metric to minimize for a weird optimization | |
| // problem, where this would be evaluated _a lot_, so I tried making this | |
| // as fast & simple as possible. | |
| pub fn area_of_superellipse(exponent: f64, semi_axes: (f64, f64)) -> f64 { | |
| let n = exponent; | |
| let (a, b) = semi_axes; | |
| // This method was derived from the following formula: |
| fn binomial_coefficient(n: u32, k: u32) -> u128 { | |
| let mut res = 1u128; | |
| for i in 0..k { | |
| res = (res * (n - i) as u128) / (i + 1) as u128; | |
| } | |
| res | |
| } | |
| fn neg_binom_pmf(k: u32, r: u32, p: f64) -> f64 { | |
| assert!(r > 0); |
| fn binomial_coefficient(n: u32, k: u32) -> u128 { | |
| let mut res = 1u128; | |
| for i in 0..k { | |
| res = (res * (n - i) as u128) / (i + 1) as u128; | |
| } | |
| res | |
| } | |
| fn neg_binom_pmf(k: u32, r: u32, p: f64) -> f64 { | |
| assert!(r > 0); |
| use num_bigint::BigUint; | |
| use num_traits::ToPrimitive; | |
| // calculate the binomial coefficient `n choose k` | |
| fn binomial_coefficient(n: u32, k: u32) -> u64 { | |
| let mut result = BigUint::from(1u32); | |
| for i in 0..k { | |
| result = (result * (n - i)) / (i + 1); | |
| } | |
| result.to_u64().unwrap() |
This post was written primarily to organize my thoughts during the design process. I'll explain the thought process that led me to each point in the design space we visit, then point out any issues, which will lead into the next iteration. Along the way, I made a few errors, which I'll try to rectify in the (Side Notes).
Here we go!
For my previous post on how many colored sources you should put in your mana bases, I adapted Frank Karsten's simulation code to the London Mulligan rules change, and to make recommendations based on how many lands you want to play. In doing so, I made two errors: first, the tables for Commander decks do not take into account the free mulligan, or the turn one draw. Second, I changed the assumed mulligan strategy to be more aggressive; the effect of this change dominated over the effect of the London Mulligan, invalidating my comment on the matter.
| use rand::prelude::*; | |
| use std::collections::HashMap; | |
| use std::fmt::Write; | |
| #[derive(Copy, Clone, PartialEq, Eq)] | |
| enum CardType { | |
| NonLand, | |
| Land, | |
| GoodLand, | |
| } |
| /* | |
| Based on Frank Karsten's "How Many Colored Mana Sources" simulation code | |
| (see his article [1], original source code found at [2]). | |
| [1]: https://strategy.channelfireball.com/all-strategy/channelmagic/channelmagic-articles/how-many-colored-mana-sources-do-you-need-to-consistently-cast-your-spells-a-guilds-of-ravnica-update/ | |
| [2]: https://pastebin.com/9P5kwqt1 | |
| Updated to account for the London Mulligan rule change, and expanded to | |
| account for more restrictive casting costs and different land counts. |