Three issues from two downstream consumers, all small, all sharing a
theme: the crate had the information and would not hand it over.
#44 — `UnknownKey { team: 0, member: 0 }` did not say which key. A
consumer upgrading 0.1.2 -> 0.4.1 had every one of 5591 predictions
return this error, fell back to a neutral 0.5, and lost its entire
metadata model for a day. Nothing crashed and nothing logged; it was
found by sweeping an unrelated parameter and noticing the output did not
move. The 0.4.0 change that made unknown keys an error was right — the
error was just too anonymous to act on. It now carries the key's `Debug`
rendering, and its `Display` says what to do about it. The precondition
is documented on every prediction entry point, which the reporter said
would alone have saved the day.
#43 — `cdf` was `pub(crate)`, so a consumer asking "is this competitor
below the cutoff" approximated it with a `mu + z * sigma` band and had no
way to say what confidence any `z` bought. Adds
`Gaussian::probability_below` / `probability_above`. The second is
separate on purpose: `1 - cdf` collapses to exactly zero past ~8.3
sigma, and a stopping rule is evaluated precisely there. Both route
through the survival function added in 0.4.1, so this is visibility
rather than new numerics.
#50 — `ConvergenceReport` was not `#[must_use]`, so the one signal that
a fit stopped short was trivially discarded. It now is, and that
immediately found 78 sites doing exactly that — including this crate's
own ATP example, which was capped at 10 sweeps when the history needs
30. The example now reads the report and says so.
`ITERATIONS = 30` is documented as the floor it is, with the three
measurements to hand: 400 events over 100 competitors already stops
there at ~7e-3 against a 1e-6 tolerance, the ATP example needs 30 at a
much looser one, and a consumer's 2000-node model needs 76 to 161.
BREAKING CHANGE: `InferenceError::UnknownKey` gains a `key` field, and
the prediction methods now require `K: Debug` in order to fill it.
Closes#43, #50. Refs #44 — its third ask, an opt-in `UnknownKeys::Skip`
mode, is a live API question and deliberately not answered here.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
Closes the last open item in #25. `cargo clippy -W missing_errors_doc
-W missing_panics_doc -W must_use_candidate -W doc_markdown` went from 56
warnings to zero.
The 13 hand-written sections name the actual variants each function returns
rather than gesturing at "an error". Establishing that meant reading the error
paths — `Game::ranked` alone returns four distinct variants, and `record_draw`
can hit TieWithoutDrawProbability where `record_winner` provably cannot, since
a two-team decisive outcome has nothing to tie. Documenting those as
interchangeable would have been worse than leaving them undocumented, because
a reader would trust it.
Two existing doc comments already described panics in prose but not under a
`# Panics` heading, so neither rustdoc nor clippy surfaced them:
`Outcome::winner` and `EventBuilder::weights`. Both now carry the heading, and
`Outcome::winner` gained the note that it ties every loser, so `n >= 3` needs a
positive p_draw — the crate's easiest error to hit by accident.
The 43 mechanical fixes (31 `#[must_use]` on pure accessors, 11 missing
backticks) were applied with `cargo clippy --fix`. `#[must_use]` on Gaussian's
arithmetic and on `posteriors()` matters: discarding those results is always a
bug, and until now nothing said so.
Also documented why `[profile.release] debug = true` exists — cargo-flamegraph
needs the symbols, and library profile settings are ignored downstream, so it
reads as an oversight without the note.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc
`Add`, `Sub`, `exclude` and `forget` combined variances by way of standard
deviations: `sigma()` takes a square root, `.powi(2)` squares it away,
`var.sqrt()` takes another, and `from_ms` squares that one back. Three roots
to compute a value that is `1/pi` all along.
They now go through `variance()` and a new `from_mv(mu, var)`, which skip
both conversions. `Sub` is the hot one — `RankDiffFactor::propagate` is
`a - b`, run for every adjacent team pair on every forward and backward
sweep of every EP iteration.
`run_chain` also stopped recomputing each team's weighted performance in the
likelihood loop; the fold is already in `arena.team_prior`, indexed by the
sorted position the loop has in hand. Each `performance()` is itself a
`forget`, so the duplicate cost scaled with players per team.
Measured on this machine, before and after, same fixtures:
Batch::iteration 23.57us -> 19.31us (-18%)
scored_history_60_events 1.071ms -> 983us (-8%)
The `Gaussian::add`/`sub` microbenchmarks cannot resolve the change: they
sit at ~234ps against a ~218ps floor that `mul`/`div` also hit, so the
harness overhead dominates a single operation.
One golden moved. Two identical competitors drawing must land on their
shared prior mean exactly, by symmetry; the root-free path now returns
25.0 where the reference transcription recorded 24.999999 — that value
rounded to six decimals. Asserting a six-decimal transcription at
epsilon 1e-6 left no headroom, so the expectation is now the exact value.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej
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>
Computes α·new + (1−α)·self in natural-parameter space. Will be used
by TruncFactor and MarginFactor to support opt-in EP damping via
ConvergenceOptions::alpha.
logaritmiskandClaude Opus 5