extern crate openblas_src;

use kickscore as ks;

fn main() {
    let mut model = ks::BinaryModel::new(ks::BinaryModelObservation::Probit);

    for player in &["A", "B", "C", "D", "E", "F"] {
        let kernel: Vec<Box<dyn ks::Kernel>> = vec![
            Box::new(ks::kernel::Constant::new(1.0)),
            Box::new(ks::kernel::Matern52::new(0.5, 1.0)),
        ];

        model.add_item(player, Box::new(kernel));
    }

    model.observe(&["A"], &["B"], 0.0);

    model.fit();

    for player in &["A", "B", "C", "D", "E", "F"] {
        let (mu, sigma) = model.item_score(player, 1.25);

        println!("{}: mu={} sigma={}", player, mu, sigma);
    }

    model.observe(&["C"], &["D"], 0.25);

    model.fit();

    for player in &["A", "B", "C", "D", "E", "F"] {
        let (mu, sigma) = model.item_score(player, 1.25);

        println!("{}: mu={} sigma={}", player, mu, sigma);
    }

    model.observe(&["E"], &["F"], 0.50);

    model.fit();

    for player in &["A", "B", "C", "D", "E", "F"] {
        let (mu, sigma) = model.item_score(player, 1.25);

        println!("{}: mu={} sigma={}", player, mu, sigma);
    }

    model.observe(&["B"], &["C"], 0.75);

    model.fit();

    for player in &["A", "B", "C", "D", "E", "F"] {
        let (mu, sigma) = model.item_score(player, 1.25);

        println!("{}: mu={} sigma={}", player, mu, sigma);
    }

    model.observe(&["D"], &["E"], 1.00);

    model.fit();

    for player in &["A", "B", "C", "D", "E", "F"] {
        let (mu, sigma) = model.item_score(player, 1.25);

        println!("{}: mu={} sigma={}", player, mu, sigma);
    }
}