fix(gaussian): treat non-positive precision as improper in mu()/sigma()

EP message cancellation can leave a Gaussian's precision (pi) a tiny negative value — round-off of exactly zero. mu()/sigma() only special-cased pi == 0, so sigma() computed 1/sqrt(pi) = NaN for pi < 0. That NaN flowed through the moment-space Sub in the game diff-chain and poisoned every skill in the slice once it grew past ~75 competitors, making converge() return all-NaN on real-scale histories (regression vs 0.1.0, which stored sigma directly). Guard pi <= 0.0 in both accessors (improper Gaussian: mu 0, sigma infinite), matching the existing pi == 0 handling. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-12 20:27:47 +02:00
parent 7742b2b891
commit e4ff46f45c
2 changed files with 102 additions and 2 deletions
@@ -0,0 +1,71 @@
+//! Regression: a single time slice with many distinct competitors must converge to finite
+//! skills. Before the `pi <= 0` guard in `Gaussian::mu()/sigma()`, EP message cancellation
+//! produced a tiny-negative precision whose `sigma() = 1/sqrt(pi)` was NaN, which the
+//! moment-space `Sub` in the game chain propagated into every skill once the slice grew past
+//! ~75 competitors (e.g. a real ranking dataset with hundreds of players).
+use trueskill_tt::{ConstantDrift, ConvergenceOptions, EPSILON, History, ITERATIONS, NullObserver};
+
+/// Tiny deterministic LCG — avoids a dev-dependency on `rand`.
+struct Lcg(u64);
+impl Lcg {
+    fn next(&mut self) -> u64 {
+        self.0 = self
+            .0
+            .wrapping_mul(6364136223846793005)
+            .wrapping_add(1442695040888963407);
+        self.0
+    }
+    fn below(&mut self, n: usize) -> usize {
+        (self.next() >> 33) as usize % n
+    }
+    fn coin(&mut self) -> bool {
+        self.next() & 1 == 0
+    }
+}
+
+fn nan_after_fit(players: usize) -> usize {
+    let mut h: History<i64, ConstantDrift, NullObserver, String> = History::builder_with_key()
+        .beta(1.0)
+        .sigma(6.0)
+        .drift(ConstantDrift(0.1))
+        .convergence(ConvergenceOptions {
+            max_iter: ITERATIONS,
+            epsilon: EPSILON,
+            ..Default::default()
+        })
+        .build();
+
+    let ids: Vec<String> = (0..players).map(|i| format!("p{i:04}")).collect();
+    let mut rng = Lcg(1);
+    for _ in 0..(players * 4) {
+        let a = rng.below(players);
+        let mut b = rng.below(players - 1);
+        if b >= a {
+            b += 1;
+        }
+        let (w, l) = if rng.coin() { (a, b) } else { (b, a) };
+        h.record_winner(&ids[w], &ids[l], 0).unwrap();
+    }
+    h.converge().unwrap();
+
+    ids.iter()
+        .filter(|id| {
+            h.current_skill(id.as_str())
+                .map(|g| !g.mu().is_finite() || !g.sigma().is_finite())
+                .unwrap_or(true)
+        })
+        .count()
+}
+
+#[test]
+fn many_competitors_converge_to_finite_skills() {
+    // The NaN regression onset was between 70 and 80 competitors; 250 is comfortably past it
+    // and in the range of a real ranking dataset.
+    for players in [12usize, 75, 150, 250] {
+        assert_eq!(
+            nan_after_fit(players),
+            0,
+            "{players}-competitor history produced NaN skills"
+        );
+    }
+}