docs: implementation plan for filtered estimates
Five tasks: delete the inert online machinery, add filtered_log_evidence,
add the two learning-curve methods, pin the invariants, record the API break.
Two spec corrections fell out of writing it. The spec claimed filtered results
would be bit-identical before and after converge(); they cannot be. iteration
recomputes the colour partition only when from == 0, so a slice built by
repeated appends keeps insertion order until the first converge() reorders it,
and the scratch clone inherits whichever order it finds — same fixed point,
different path. Corrected to agreement within 1e-8 under tight convergence,
matching the house pattern in tests/ingestion_equivalence.rs. The spec also
declared filtered_pass as Vec<(T, Vec<(Index, Gaussian)>)>, which cannot carry
the evidence its own step 3 harvests; it returns Vec<(T, FilteredStep)>.
CHANGELOG.md is generated by git-cliff, so the spec's "CHANGELOG records the
API break" cannot be satisfied by editing the file — it regenerates. Task 5
records the break through the commit subject and verifies the generated output
instead. cliff.toml has no breaking-change parser at all, which the task is
told to report rather than work around.
An adversarial reviewer checked the plan against the source before this commit
and found four real defects, all in plan text, none in the design:
- Two prescribed mutations provably could not fail their named tests. The
learning-curve mutation altered only what filtered_pass writes after a slice,
while the test inspected filtered[0], which is computed from an empty message
map. Fixed by asserting monotonic mu across the whole curve.
- The ingestion-order fixture used four distinct timestamps, giving one event
per slice — the exact degenerate shape ingestion_equivalence.rs documents as
the weak case, making the assertion true by construction. Fixed to several
events per timestamp with shared competitors.
- filtered_learning_curves was never asserted for content, only for emptiness
on an empty history.
- A doc comment restated learning_curves' claim that key(idx) is O(n) and the
method O(n^2). KeyTable::key is self.reverse.get(idx.0) — O(1) — and the
type's own doc says so. The claim predates reverse becoming a Vec. The plan
now corrects the original at history.rs:323 rather than copying it.
The reviewer confirmed the central claim by tracing the call graph: N_INF is
{pi: 0, tau: 0} and Mul is a natural-parameter add, so it is an exact
multiplicative identity, and the only write to skill.backward in the crate is
in new_backward_info, reachable only from History::iteration and never from
iterate_to_convergence under either rayon cfg.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc
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Claude Opus 5