`gamma` enters only as `gamma * gamma`, so the sign was squared away:
measured against the old public-field form, `ConstantDrift(-0.0833)`
produced results bit identical to `ConstantDrift(0.0833)`. The sign was
neither rejected nor honoured — it vanished.
It could not be checked while the field was a public tuple position,
because there was nothing to intercept. Validating inside
`variance_for_elapsed` would have been worse: it runs in the sweep, so a
construction-time mistake would panic mid-inference, and `Gaussian::from_ms`
is a worked example of why that is the wrong place — rejecting NaN there
turned the NonFiniteResult reporting path into a crash.
So `ConstantDrift::new` is the only way in and it checks, with `gamma()`
to read the value back. 129 call sites rewritten across src, tests,
benches, examples and the README. The dated plan and spec documents under
docs/superpowers are left alone: they record what was built at the time,
and rewriting them would falsify that.
tests/constructor_validation.rs is the more valuable half. This defect
class was closed three times in one session and reopened twice, because
each fix validated the layer it had just touched and inferred the rest —
`HistoryBuilder`, then `Game`'s own entry points, then the constructors
beneath both. A per-site fix cannot notice the site nobody thought of, so
that file enumerates every public entry point taking a magnitude and
asserts each refuses negative and non-finite values.
It found an eleventh defect on its first run: `HistoryBuilder::score_sigma`
accepted infinity, because `inf > 0.0` is true and the assert only tested
positivity. Fixed, and its own `should_panic` message updated to match.
`Gaussian::from_ms` is deliberately exempt from the non-finite half, for
the reason above: a broken fit produces a NaN sigma legitimately and
`converge` must be allowed to report it.
The convergence-level drift-variance check stays and is now tested through
a custom `Drift` implementation, since `ConstantDrift` can no longer reach
it. That check is the only thing standing between a third-party `Drift`
and a NaN fit.
BREAKING CHANGE: `ConstantDrift`'s field is private. Replace
`ConstantDrift(x)` with `ConstantDrift::new(x)`, and `drift().0` with
`drift().gamma()`. `HistoryBuilder::score_sigma` now rejects infinity.
Closes#65
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
#46: every accessor returns a per-competitor marginal, and almost
nothing a consumer publishes is one competitor. Combining marginals
assumes independence, and competitors are correlated through every event
they share.
`posterior_of(&[(a, 1.0), (b, -1.0)])` returns the posterior of that
combination with the correlation intact. Validated against the exact
linear-Gaussian posterior on both a tree and a loopy fixture, for
differences and for single competitors: agreement to 1e-9 relative in
every case.
The investigation that preceded this is why it is not a covariance
accessor. Marginals from loopy message passing are about half the true
width, and ignoring correlation overstates a difference — the two errors
partially cancel, leaving 1.327x rather than 2.646x. Bolting true
correlations onto the existing marginals would have given 0.765 against
a true 1.524, which is overconfident: the direction the reporter
specifically called unsafe. Rebuilding the joint from the factor
structure fixes both at once, and a single-competitor query now returns
the exact marginal rather than the narrow one.
The precision matrix depends only on structure — who played whom, with
what weights and what noise — not on the observed outcomes, and the
means were already exact. So only the second moment is reconstructed.
Known limits, all deliberate and documented on the method:
- Latest slice only. A functional spanning times, such as "current
versus career", needs the time-expanded joint and is not covered.
- Scored events only. A ranked outcome's truncation is EP-approximated
and its converged factors are not retained after inference, so ranked
slices return `JointUnavailable` rather than a plausible wrong number.
- Dense Cholesky, O(n^3) per query in the slice's competitor count:
38.8us at 50, 5.66ms at 400, 49.1ms at 800. Fine for the sizes this
serves today; caching the factorization per slice would make repeat
queries O(n^2), and sparsity is the next step after that.
Refs #46, #47, #48
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
clippy::useless_vec. Pushed broken because the verification chain used
`if ... then echo` blocks that report status without gating the `&&`
that follows — the same shape of mistake as e1bddf2, where a grep
succeeded on the error text. Both times the check printed FAIL and the
push went ahead.
The reliable form is a single fail-fast chain:
just lint && just test && just determinism && cargo test --doc
so nothing runs after the first failure.
Investigation for #46 and #47, before touching either.
A scored history is linear-Gaussian, so its true joint posterior has a
closed form and the crate can be checked against ground truth. Measured
on five competitors:
means marginal sd (crate / exact)
tree (star) exact 1.000
loopy (robin) exact 0.502
On a tree the crate is exact in both. With cycles the means stay exact —
the standard Gaussian-BP result, and the property ratings rely on —
while marginal variances come out about half the true width.
That is the opposite direction from what #47 reports, so whatever is
happening in that consumer's model, the crate being conservative is not
it.
It also means #46 cannot be implemented as an added covariance accessor.
The exact correlation between two nodes here is +0.857, so ignoring it
overstates the width of a difference — but the too-narrow marginals
partially cancel that, leaving 1.327x rather than 2.646x. Adding true
correlations to these marginals without correcting them would give 0.765
against a true 1.524: overconfident, which is the direction the reporter
specifically called unsafe.
Pins the two real invariants (exactness on a tree, exact means with
cycles) and deliberately only records the variance gap, since closing it
is what #46 proposes.
Also records the working rules this project has converged on: investigate
before implementing, fix the root issue, and scout crates.io on measured
accuracy rather than adoption.
Refs #46, #47
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
logaritmiskandClaude Opus 5