logaritmiskandClaude Opus 5
b553c630f5
`K` is the one type parameter people change, and it was last. Naming a
history in a struct field meant writing all four to say one thing:
struct Ladder { history: History<i64, ConstantDrift, NullObserver, String> }
struct Analysis<'h> { joint: Joint<'h, i64, ConstantDrift, NullObserver, &'static str> }
Now:
struct Ladder { history: History<String> }
struct Analysis<'h> { joint: Joint<'h> }
`History<K, T, D, O>`, all four defaulted. Bounds may reference later
parameters, so `D: Drift<T> = ConstantDrift` is legal in third position.
`Joint` gains the same defaults, so `Joint<'h, String>` spells it.
72 call sites swapped, and the reorder makes most of them shorter: 18
now read `History<String>` and the `&'static str` ones read `History`.
The two turbofished builders shrink from
`HistoryBuilder::<Untimed, _, _, String>::new()` to
`HistoryBuilder::<String, Untimed>::new()`.
`Joint` keeps `O` structurally, defaulted rather than removed. #72 notes
it never touches the observer, which is true — but it borrows the whole
`&'h History<K, T, D, O>` and calls `History::resolve_terms`, so dropping
the parameter means either a view type or moving that method off
`History`. The default already buys the entire user-visible benefit,
which was the spelling.
Refs #72.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
73 lines
2.3 KiB
Rust
73 lines
2.3 KiB
Rust
//! Regression: a single time slice with many distinct competitors must converge to finite
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//! skills. Before the `pi <= 0` guard in `Gaussian::mu()/sigma()`, EP message cancellation
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//! produced a tiny-negative precision whose `sigma() = 1/sqrt(pi)` was NaN, which the
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//! moment-space `Sub` in the game chain propagated into every skill once the slice grew past
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//! ~75 competitors (e.g. a real ranking dataset with hundreds of players).
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use trueskill_tt::{ConstantDrift, ConvergenceOptions, EPSILON, History, ITERATIONS};
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/// Tiny deterministic LCG — avoids a dev-dependency on `rand`.
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struct Lcg(u64);
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impl Lcg {
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fn next(&mut self) -> u64 {
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self.0 = self
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.0
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.wrapping_mul(6364136223846793005)
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.wrapping_add(1442695040888963407);
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self.0
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}
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fn below(&mut self, n: usize) -> usize {
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(self.next() >> 33) as usize % n
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}
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fn coin(&mut self) -> bool {
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self.next() & 1 == 0
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}
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}
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fn nan_after_fit(players: usize) -> usize {
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let mut h: History<String> = History::builder()
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.key_type::<String>()
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.beta(1.0)
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.sigma(6.0)
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.drift(ConstantDrift::new(0.1))
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.convergence(ConvergenceOptions {
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max_iter: ITERATIONS,
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epsilon: EPSILON,
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..Default::default()
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})
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.build();
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let ids: Vec<String> = (0..players).map(|i| format!("p{i:04}")).collect();
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let mut rng = Lcg(1);
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for _ in 0..(players * 4) {
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let a = rng.below(players);
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let mut b = rng.below(players - 1);
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if b >= a {
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b += 1;
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}
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let (w, l) = if rng.coin() { (a, b) } else { (b, a) };
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h.record_winner(&ids[w], &ids[l], 0).unwrap();
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}
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let _ = h.converge().unwrap();
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ids.iter()
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.filter(|id| {
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h.current_skill(id.as_str())
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.map(|g| !g.mu().is_finite() || !g.sigma().is_finite())
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.unwrap_or(true)
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})
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.count()
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}
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#[test]
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fn many_competitors_converge_to_finite_skills() {
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// The NaN regression onset was between 70 and 80 competitors; 250 is comfortably past it
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// and in the range of a real ranking dataset.
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for players in [12usize, 75, 150, 250] {
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assert_eq!(
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nan_after_fit(players),
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0,
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"{players}-competitor history produced NaN skills"
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);
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}
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}
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