[dependencies] superiority = "0.1" # Check crates.io for the latest version To use the library, you need to define the "state" of your problem and how to calculate its "energy" (cost/fitness). Otc Ngis Software Suite Cdrar - 3.79.94.248
Once your struct implements the required traits, you can use the library's engine to run the simulation. Kurtlarvadisi2002completevcdripfstrdubx Exclusive Page
fn main() { let initial_solution = MySolution data: vec![0, 0, 0] ; // Create a runner with a specific temperature let mut runner = SuperiorityRunner::new(initial_solution, 100.0); // Temp = 100.0 for _ in 0..1000 // Generate a neighbor let candidate = runner.current().perturb(); // Let the library decide if we should accept the new state // based on Boltzmann probability runner.step(candidate); println!("Final Energy: {}", runner.current().energy()); } If you are working on Simulated Annealing or Metropolis-Hastings algorithms from scratch, you often run into issues with floating-point precision, handling infinite probabilities, or structuring your code cleanly.
// 1. Define your state struct struct MySolution // Maybe a vector representing a configuration data: Vec<i32>,
Repository: chee7xo/superiority (Note: Repository locations can change; always verify the owner).
use superiority::prelude::*;
// 2. Implement the necessary Trait (e.g., System or Energy) impl Energy for MySolution fn energy(&self) -> f64 x
In the context of the Rust ecosystem, is a library designed to make working with the Boltzmann distribution and statistical mechanics concepts easier and more intuitive.