`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
#49: `expected_information_gain` enumerates discrete outcomes, so a
consumer recording continuous scores cannot ask which matchup to run
next. The issue flagged this as possibly a research question, since
"expected variance reduction under EP may not have a clean closed form
even for Gaussian likelihoods".
It does. Observing a scored event is a rank-one update to the precision
matrix, so Sherman-Morrison gives
reduction = (c^T L^-1 a)^2 / (v + a^T L^-1 a)
for target functional c and matchup contrast a. Verified against an
actual refit on four candidate matchups: agreement to 1e-9 relative.
Two consequences worth stating.
There is no expectation to take. The expression depends on which matchup
is played but not on how it turns out, because for a Gaussian likelihood
the posterior variance update is data-independent. Pinned by
`the_outcome_does_not_change_the_reduction`, which refits with scores of
(3, 1), (100, -50) and (0, 0) and gets the same answer. The name keeps
the term the active-learning literature uses; no averaging happens.
It is also far cheaper than its ranked counterpart — one linear solve
rather than a full inference pass per possible outcome — because
`c^T L^-1 a` and `a^T L^-1 a` share the same solve.
`target` is deliberately the same linear-functional shape as
`posterior_of`, as the issue proposed, so the two share a concept rather
than inventing two.
The load-bearing test is the refit comparison. An acquisition function
is the archetype of a surface that returns finite, plausible, monotone
numbers while being wrong, and then quietly selects worse matchups
forever; ranking behaviour alone would not catch that.
Closes#49
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
logaritmiskandClaude Opus 5