Anders Olsson
8b53cacd64
Adds soft Gaussian-observation evidence on the per-pair diff variable,
enabling continuous score margins as a richer alternative to ranks.
Public API:
- `Outcome::Scored([scores])` (non-breaking enum extension under
`#[non_exhaustive]`).
- `Game::scored(teams, outcome, options)` constructor parallel to
`Game::ranked`.
- `EventBuilder::scores([...])` fluent helper.
- `HistoryBuilder::score_sigma(σ)` knob (default 1.0, validated > 0).
- `GameOptions::score_sigma`.
- `EventKind` re-exported from `lib.rs` (annotated `#[non_exhaustive]`).
- New `InferenceError::InvalidParameter { name, value }` variant.
Internals:
- `MarginFactor` (`factor/margin.rs`): Gaussian observation factor that
closes in one EP step; cavity-cached log-evidence mirrors `TruncFactor`.
- `BuiltinFactor::Margin` dispatch arm.
- `DiffFactor` enum in `game.rs` lets `Game::likelihoods` and the new
`likelihoods_scored` share the per-pair link abstraction.
- Per-event `EventKind { Ranked, Scored { score_sigma } }` routed through
`TimeSlice::add_events`, `iteration_direct`, and `log_evidence`.
Tests: 88 lib + 27 integration (4 new in `tests/scored.rs`); existing
goldens byte-identical. Bench: `benches/scored.rs` baseline ~960µs for
60 events × 20-player pool with default convergence.
Plan: docs/superpowers/plans/2026-04-27-t4-margin-factor.md
Spec item marked Done.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
60 lines
1.9 KiB
Rust
60 lines
1.9 KiB
Rust
//! Worked example: continuous-score outcomes via `Outcome::Scored`.
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//!
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//! Three players play a small round-robin where the score margin matters,
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//! not just who won. We show how `score_sigma` controls how much weight
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//! the engine places on the observed margin.
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//!
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//! Run with: `cargo run --example scored --release`
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use smallvec::smallvec;
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use trueskill_tt::{ConstantDrift, Event, History, Member, Outcome, Team};
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fn main() {
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.drift(ConstantDrift(0.03))
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.score_sigma(2.0) // tune to data; smaller = trust margins more
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.build();
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let events: Vec<Event<i64, &'static str>> = vec![
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Event {
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time: 1,
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teams: smallvec![
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Team::with_members([Member::new("alice")]),
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Team::with_members([Member::new("bob")]),
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],
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outcome: Outcome::scores([21.0, 9.0]),
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},
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Event {
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time: 2,
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teams: smallvec![
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Team::with_members([Member::new("bob")]),
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Team::with_members([Member::new("carol")]),
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],
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outcome: Outcome::scores([21.0, 18.0]),
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},
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Event {
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time: 3,
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teams: smallvec![
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Team::with_members([Member::new("alice")]),
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Team::with_members([Member::new("carol")]),
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],
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outcome: Outcome::scores([21.0, 21.0]),
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},
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];
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h.add_events(events).unwrap();
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let report = h.converge().unwrap();
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println!(
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"converged={}, iterations={}, log_evidence={:.4}",
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report.converged, report.iterations, report.log_evidence
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);
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for who in &["alice", "bob", "carol"] {
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let s = h.current_skill(who).unwrap();
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println!("{:>6}: mu={:>7.3} sigma={:.3}", who, s.mu(), s.sigma());
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}
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}
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