| # A graph as a vector of sorted vectors. This is very similar to the way sparse matrices | |
| # are represented except each vertex has its own sorted vector (making it much faster to | |
| # add/remove edges). Using BTrees instead of sorted vector would improve performance for | |
| # adding/removing edges. | |
| #WeightedGraph = Vector{Vector{(UInt32, Float64)}} | |
| Graph = Vector{Vector{UInt32}} # Make more generic... | |
| # There has to be a better way (map?): | |
| function make_graph(order) |
Most editors/IDEs have good Rust support at this point, but I recommend Visual Code with Rust Analyzer.
Simply copy and paste the command from the rustup website (here) in a terminal. Rustup will install the rustc compiler and, just as importantly, the cargo package manager.
| /// You need to add these lines to Cargo.toml's [dependencies]: | |
| /// statrs = "0.15.0" | |
| /// nalgebra = "0.29.0" | |
| use statrs::distribution::{Binomial, Discrete}; | |
| use nalgebra::base::DVector; | |
| use nalgebra::dvector; | |
| // Builds a vector of evenly spaced floats within a closed interval [a, b]. | |
| pub fn linspace(a: f64, b: f64, n: usize) -> DVector<f64> { |
| fn nth_primes(n: usize) -> usize { | |
| match n { | |
| 0 => 1, | |
| 1 => 2, | |
| 2 => 3, | |
| _ => { | |
| let mut primes = vec![2, 3]; | |
| let mut candidate = 5; | |
| loop { | |
| let max = (candidate as f64).sqrt() as usize; |
| def hello := "world" | |
| open List | |
| #check Nat.zero | |
| #check Nat.succ | |
| #check Nat.succ 0 | |
| #check Nat.add | |
| #check Nat.add 1 | |
| #check Nat.add 2 2 |
| import data.real.basic | |
| #check ℝ | |
| open nat | |
| -- Links: | |
| -- * Lean 3's big math library: https://github.com/leanprover-community/mathlib | |
| -- * Number game: https://www.ma.imperial.ac.uk/~buzzard/xena/natural_number_game/ | |
| -- * Agda's Unimath library: https://github.com/UniMath/agda-unimath |
