//! Forward-only (filtering) estimates: what the model knew at the time,
//! as opposed to the smoothed posteriors `learning_curve` reports.

use smallvec::smallvec;
use trueskill_tt::{Event, History, Member, Outcome, Team};

/// `games` one-on-one matches at successive times, won by "a" every time.
///
/// This is the fixture from issue #19, where `online(true)` reported
/// `games * ln(0.5)`.
fn repeated_winner(games: i64) -> History {
    let mut history = History::builder().build();

    for time in 1..=games {
        history
            .add_events([Event {
                time,
                teams: smallvec![
                    Team::with_members([Member::new("a")]),
                    Team::with_members([Member::new("b")]),
                ],
                outcome: Outcome::winner(0, 2),
            }])
            .unwrap();
    }

    history
}

#[test]
fn filtered_evidence_sits_between_coin_flip_and_batch() {
    let mut history = repeated_winner(5);

    history.converge().unwrap();

    let coin_flip = 5.0 * 0.5f64.ln();
    let batch = history.log_evidence();
    let filtered = history.filtered_log_evidence();

    assert!(
        filtered > coin_flip,
        "filtered evidence {filtered} is at or below {coin_flip}, the all-coin-flip \
         value the inert online flag reported; game one is a coin flip but games two \
         through five are not"
    );

    assert!(
        filtered < batch,
        "filtered evidence {filtered} is not below the smoothed {batch}; filtering \
         scores each game on strictly less information than smoothing does"
    );
}