muxer 0.5.2

Deterministic, multi-objective bandit-style routing primitives (windowed stats + Pareto selection).
Documentation 
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#[cfg(not(feature = "contextual"))]
fn main() {
    eprintln!("This example requires: cargo run --example contextual_router --features contextual");
}

#[cfg(feature = "contextual")]
fn main() {
    use muxer::{LinUcb, LinUcbConfig};
    use rand::rngs::StdRng;
    use rand::Rng;
    use rand::SeedableRng;

    let arms = vec!["small".to_string(), "big".to_string()];

    // Toy "context": two features
    // - x0: prompt length proxy
    // - x1: "difficulty" proxy
    let cfg = LinUcbConfig {
        dim: 2,
        lambda: 1.0,
        alpha: 1.0,
        seed: 0,
        decay: 1.0,
    };
    let mut pol = LinUcb::new(cfg);

    // Simulated environment: big model does better when difficulty is high, but has same reward otherwise.
    let mut env = StdRng::seed_from_u64(123);

    for t in 0..2_000u64 {
        let prompt_len = env.random_range(0.0..2000.0);
        let difficulty = env.random::<f64>(); // 0..1
        let ctx = [prompt_len / 2000.0, difficulty];

        let d = pol.decide(&arms, &ctx).unwrap();
        let chosen = d.chosen.as_str();
        let scores = pol.scores(&arms, &ctx);

        // Reward: success probability depends on context and chosen arm.
        let p_success = match chosen {
            "small" => 0.70 - 0.25 * difficulty,
            "big" => 0.70 - 0.05 * difficulty,
            _ => 0.5,
        }
        .clamp(0.0, 1.0);

        let reward = if env.random::<f64>() < p_success {
            1.0
        } else {
            0.0
        };
        pol.update_reward(chosen, &ctx, reward);

        if t % 200 == 0 {
            eprintln!(
                "t={:4} ctx={:?} decision={:?} reward={} scores={:?}",
                t, ctx, d, reward, scores
            );
        }
    }
}