ganesh 0.26.2

Minimization and sampling in Rust, simplified
Documentation 
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use fastrand::Rng;
use ganesh::{
    algorithms::particles::{
        pso::PSOConfig, Swarm, SwarmPositionInitializer, TrackingSwarmObserver, PSO,
    },
    core::{Callbacks, MaxSteps},
    traits::{Algorithm, CostFunction},
    DVector, Float, PI,
};
use std::{convert::Infallible, error::Error, fs::File, io::BufWriter, path::Path};

fn main() -> Result<(), Box<dyn Error>> {
    // Define the function to sample (a multimodal distribution)
    struct Problem;
    // Implement Rastrigin function
    impl CostFunction for Problem {
        fn evaluate(&self, x: &DVector<Float>, _args: &()) -> Result<Float, Infallible> {
            Ok(10.0
                + (x[0].powi(2) - 10.0 * Float::cos(2.0 * PI * x[0]))
                + (x[1].powi(2) - 10.0 * Float::cos(2.0 * PI * x[1])))
        }
    }
    let problem = Problem;

    // Create and seed a random number generator
    let mut rng = Rng::new();
    rng.seed(0);

    // Create a tracker to record swarm history
    let tracker = TrackingSwarmObserver::new();

    // Create a particle swarm optimizer algorithm and set some hyperparameters
    // Run the particle swarm optimizer
    let mut pso = PSO::default();
    let init = Swarm::new(SwarmPositionInitializer::RandomInLimits {
        bounds: vec![(-20.0, 20.0), (-20.0, 20.0)],
        n_particles: 50,
    });
    let config = PSOConfig::default()
        .with_c1(0.1)?
        .with_c2(0.1)?
        .with_omega(0.8)?;
    let result = pso.process(
        &problem,
        &(),
        init,
        config,
        Callbacks::empty()
            .with_observer(tracker.clone())
            .with_terminator(MaxSteps(200)),
    )?;

    println!("{}", result);

    // Export the results to a Python .pkl file to visualize via matplotlib
    let mut writer = BufWriter::new(File::create(Path::new("data.pkl"))?);
    serde_pickle::to_writer(&mut writer, &tracker, Default::default())?;
    Ok(())
}