Use when you are about to touch, review, or debug any probability/statistics math in Area Occupancy Detection (priors, likelihoods, decay half-lives, logit-space boosts, correlation confidence, timezone/interval bucketing) and need to hand-verify the numbers BEFORE trusting a PR, a bug report, or your own patch. Trigger on tasks like "does this prior look right", "is this decay curve correct", "why did the prior pin at 0.99", "check this ratio/denominator", "audit this new coupling between areas for feedback loops", or any request to prove a calculation from first principles rather than just running the test suite.
Installation
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Use when you are about to touch, review, or debug any probability/statistics math in Area Occupancy Detection (priors, likelihoods, decay half-lives, logit-space boosts, correlation confidence, timezone/interval bucketing) and need to hand-verify the numbers BEFORE trusting a PR, a bug report, or your own patch. Trigger on tasks like "does this prior look right", "is this decay curve correct", "why did the prior pin at 0.99", "check this ratio/denominator", "audit this new coupling between areas for feedback loops", or any request to prove a calculation from first principles rather than just running the test suite.
AOD Proof-and-Analysis Toolkit
What this covers
Seven first-principles verification recipes for this repo's probability math: hand-computing
a prior from raw intervals, checking a logit-space boost's algebra, auditing a decay curve,
detecting UTC/local timezone mixing, judging whether learned data is statistically sufficient
to trust, auditing any ratio's numerator/denominator for window consistency, and analyzing a
new area-to-area coupling for self-reinforcing feedback. Each recipe is a runnable check, not
just a formula — copy the Python one-liners, run them, compare to the worked example. Use this
skill whenever you need to prove a number is right, not just observe that a test passed
(tests can encode the same bug they're supposed to catch — see Recipe 1).
When NOT to use this: for the underlying formulas' definitions and why they're shaped
this way, see bayesian-occupancy-reference. For the day-to-day campaign of collecting real-home
data and tuning constants, see aod-learning-accuracy-campaign. For a narrative of past bugs and
their fixes (more sagas, less how-to), see aod-failure-archaeology. For open research questions
beyond the fixes below, see aod-research-frontier.
Recipe 1 — Hand-compute a prior from raw intervals
What it proves: that global_prior = occupied_duration / observation_period uses a
period that actually reflects reality, not a truncated window.
Steps:
Find every occupied interval (start, end) for the area in the lookback window.
Compute occupied_duration = Σ (end - start) in seconds.
Compute the observation period as (first_interval_start, actual_period_end). As of PR #491
(merged 2026-07-06), actual_period_end on main is always now (see worked example below
for the bug this replaced).
Sanity-check against the reporter's real-world estimate. If the computed prior is pinned at
0.99 (or 0.01) while a human says "this room is occupied ~30% of the time," the period
window is wrong — go straight to Recipe 6.
Worked example — issue #483, fixed by PR #491 (merged 2026-07-06)
(custom_components/area_occupancy/data/analysis.py:513-520,
test tests/test_data_analysis.py::test_valid_calculation_sets_correct_prior):
Fixture: one occupied interval (now - 8h, now - 6h) — 2 hours occupied, and the area has been
quiet for 6 hours since.
Quantity
Buggy (pre-fix, before PR #491)
Correct (current main, PR #491's fix)
first_interval_start
now − 8h
now − 8h
actual_period_end
last_interval_end = now − 6h (because (now - last_interval_end) > 3600s triggers a truncation branch)
now
actual_period_duration
2h
8h
occupied_duration
2h
2h
prior
2h / 2h = 1.0 → clamped to 0.99
2h / 8h = 0.25
The buggy code drops the "quiet tail" (the 6 hours of known non-occupancy since the interval
ended) from the denominator every time an area has been quiet more than 1 hour — which is
every night, every workday, every weekend. Each hourly pipeline run during a quiet stretch
recomputes the prior over a shrinking window, so it walks toward 1.0/0.99 monotonically. This
is why a kitchen with a true 28–35% occupancy rate was observed pinned at 0.99 (the original
bug report).
When to run this: before trusting any prior-related bug report ("prior stuck at 0.99/0.01",
"prior doesn't match how often I'm actually in the room"); before merging any change to
PriorAnalyzer.calculate_and_update_prior(); whenever a test asserts a specific prior value —
recompute it by hand first, because a test can encode the bug it's meant to catch (this is
exactly what happened here: test_valid_calculation_sets_correct_prior asserted 0.99 for
years before anyone hand-checked it).
Recipe 2 — Logit-space algebra check for a boost
What it proves: that a "boost" (any additive adjustment applied in log-odds space) produces
the probability shift you think it does, given its stated gain.
Background you need: logit(p) = ln(p/(1-p)), clamped to p ∈ [0.01, 0.99] before taking
the log so it never diverges (custom_components/area_occupancy/utils.py:123-133,
clamp_probability at utils.py:45-75). sigmoid(z) = 1/(1+e^-z) inverts it. Any boost of the
form new_logit = logit(base) + contribution then new_prob = sigmoid(new_logit) is "logit-space"
because it adds in log-odds, not probability — a +1.0 contribution is a much bigger swing
near p=0.5 than near p=0.9.
Steps:
Identify the contribution formula and its gain constant.
Compute logit(reference_probability) by hand (or Python).
Multiply by the gain to get contribution.
Add to logit(base_probability) and run through sigmoid to get the shifted probability.
Compare against the code's own diagnostic field (e.g. logit_contribution in the
BoostContribution dataclass) if you have a live diagnostics dump.
Worked example — adjacent-areas Bayesian boost (custom_components/area_occupancy/data/adjacency.py:122-204,
constant ADJACENCY_BOOST_GAIN = 0.5 at const.py:206). This feature (PR #454, merged to main
2026-07-06) is now on main — the adjacency feature remains unvalidated on real homes, so treat
its constants as candidates, not settled tuning.
The formula is logit_contribution = gain × (logit(P) − logit(0.5)). Since logit(0.5) = 0,
this reduces to gain × logit(P) (the centring term is documented as a deliberate no-op, kept
for clarity — see the comment at adjacency.py:115-118).
Say the learned transition probability P (that the household moves into this area from its
neighbour) is 0.9, and the area's own base probability before the boost is 0.5:
Result: logit(0.9) ≈ 2.1972, gain-scaled contribution ≈ 1.0986, and a 0.5 base probability
is pushed to exactly 0.75 — a fixed ~1.0986 logit-space nudge is a big swing at p=0.5 (25
points) but shrinks fast near the clamped edges (p=0.9 → ~0.947, only +4.7 points) because
sigmoid saturates. This nonlinearity is the whole point of doing it in logit space — it means
the same boost can never push a confident "occupied" reading past ~0.99 or a confident "empty"
reading below ~0.01, but it can meaningfully swing an uncertain 0.5.
When to run this: before merging any change to a gain/weight constant that's applied in
logit space (ADJACENCY_BOOST_GAIN, strength_multiplier, activity occupancy_boost, the
0.8/0.2 combined-probability weighting in combined_probability()); whenever a bug report says
"the boost seems too strong/weak" — hand-compute the actual percentage-point shift at the
probabilities in question, don't reason about the gain number in isolation; before trusting any
new "influence weight" or "confidence multiplier" config surface.
Recipe 3 — Decay curve audit
What it proves: that decay_factor at a given elapsed time matches 0.5^(age/half_life),
and that the 5% floor cutoff fires at the right elapsed time.
Formula (custom_components/area_occupancy/data/decay.py:148-152):
Get half_life (seconds) and age (seconds since the last evidence / decay start).
Compute 0.5 ** (age / half_life) by hand.
Compare to the code's live value (diagnostics entities[].decay field, or unit test table
below).
If auditing "when does this practically stop mattering", solve for the cutoff:
age = half_life × ln(0.05)/ln(0.5) ≈ half_life × 4.3219.
Worked example — from tests/test_data_decay.py::test_decay_factor, half_life=60.0:
age (s)
age / half_life
expected decay_factor
0
0.00
1.0
15
0.25
0.8409
30
0.50
0.7071
45
0.75
0.5946
60
1.00
0.5
90
1.50
0.3536
120
2.00
0.25
258
4.30
0.0501 (just above the floor)
260
4.33
0.0 (below 0.05, floored)
print(0.5 ** (30/60)) # 0.7071067811865476print(0.5 ** (258/60)) # 0.05068...print(0.5 ** (260/60)) # 0.04943... -> below 0.05, code returns 0.0import math
print(math.log(0.05)/math.log(0.5)) # 4.321928094887363 half-lives to cross the floor
At a 60s half-life, the floor trips at ≈259.3s (60 × 4.3219), matching the table's boundary
between 258s (still non-zero) and 260s (floored). This 4.32-half-life constant is universal
— it's independent of the actual half-life value, so you can sanity-check any half-life's
"effectively done decaying" time by multiplying by 4.32 (e.g. Bedroom's sleeping half-life of
1200s effectively floors at ~5186s ≈ 86 minutes).
Special-cased half-lives to know when auditing (data/entity.py, data/purpose.py:160-236,
const.py:144-146): Wasp-in-Box entities use half_life = 0.1s (effectively no decay — clears
in under half a second); sleep-presence virtual entities use SLEEP_PRESENCE_HALF_LIFE = 7200s
(2 hours, "persistent presence"); both bypass purpose/sleep-window switching entirely
(purpose=None is passed to Decay). The SLEEPING purpose additionally multiplies its
resolved half-life by an adjacency _modifier_factor (clamped ≥ 1.0, cap ADJACENCY_DECAY_MODIFIER_MAX = 1.75 — see Recipe 7) — when auditing a reported half-life against the configured one,
check Decay.half_life (the multiplied, effective value) vs Decay._base_half_life (what the
user actually set), since PR #493 (merged 2026-07-06, fixed issue #481) exists precisely
because this distinction was collapsed for Bedroom areas outside the sleep window — main now
carries the _resolve_purpose_half_life() guard (!= purpose default → return base) plus the
adjacency modifier_factor multiplying on top.
When to run this: whenever a user reports a room "clearing" (occupancy flipping to
unoccupied) faster or slower than expected; before changing any purpose's default half-life in
data/purpose.py; before merging any change to Decay.decay_factor or Decay.half_life; when
auditing whether a custom half-life is actually being honored (compare _base_half_life to the
purpose default — see issue #439/#481 pattern in aod-failure-archaeology).
Recipe 4 — Interval/timezone audit (UTC vs local mixing)
What it proves: whether a reported anomaly (negative durations, off-by-N-hours bucketing,
priors that look shifted) is caused by mixing timezone-aware and naive/local datetimes.
Policy this repo commits to (custom_components/area_occupancy/time_utils.py:1-8):
Runtime arithmetic/comparisons: timezone-aware UTC.
Database persistence (SQLite): naive UTC (tzinfo=None, interpreted as UTC).
Wall-clock bucketing (time priors, daily/weekly/monthly grouping): HA local timezone.
Any code path that accidentally does arithmetic between a naive-assumed-local value and an
aware-UTC value produces an error equal to the local UTC offset — and that's the discriminating
signature.
The discriminating check: a timezone-mixing bug shows up as an error that is a suspiciously
round number of hours — the reporter's UTC offset (e.g. exactly ±3h, ±5h, ±5.5h for
half-hour-offset zones), not a random float, and not exactly zero. Compare against the
reporter's timezone in Home Assistant (hass.config.time_zone) or the browser locale they
mention.
Worked example — issue #301 ("Invalid period duration errors in 2025.12.2", closed
2025-12-29, fixed by 2025.12.4 alongside commit 3dcb6f1 "Implement timezone normalization and
local bucketing utilities" which introduced time_utils.py): logs showed
Invalid period duration (-10800.00 seconds) for area Hallway and
(-10797.30 seconds). -10800s = -3.0h exactly; the second occurrence is ~3h plus a few seconds
of clock skew. The reporter confirmed their HA timezone was US Eastern. The lesson to copy:
a clean or near-clean multiple-of-3600-seconds error is the signature to search for — grep your
own reported "invalid duration"/"negative period" logs for values divisible by 3600 (or 1800 for
half-hour zones) before looking anywhere else. This exact bug class was closed by introducing
time_utils.py (commit 3dcb6f1, 2025-12-12) as the single source of truth for UTC/local
conversion — if you find a new raw datetime subtraction outside that module, treat it as a
prime suspect.
How to reproduce/detect this class of bug yourself:
from homeassistant.util import dt as dt_util
from datetime import timedelta
# Simulate what a naive-vs-aware subtraction looks like:
aware_now = dt_util.utcnow()
naive_stored = aware_now.replace(tzinfo=None) - timedelta(hours=3) # e.g. read from SQLite as naive-local# If code does `aware_now - naive_stored` it raises TypeError (good — caught fast).# If code does `aware_now - naive_stored.replace(tzinfo=dt_util.UTC)` when naive_stored# was actually local (not UTC), you get a silent 3h-shaped error instead of a crash — this is the# dangerous case, because it doesn't raise, it just quietly biases every duration/bucket by# the local UTC offset.
Checklist for any new datetime-touching code:
Every persisted timestamp read from the DB passes through from_db_utc() before use.
Every timestamp written to the DB passes through to_db_utc().
Every runtime comparison/subtraction operates on values that went through to_utc().
Bucketing (day-of-week, hour-of-day, daily/weekly rollups) explicitly calls to_local()
— never buckets on raw UTC hour, or DST transitions shift every bucket by an hour twice a
year.
If you must iterate hour-by-hour across a DST boundary, iterate in UTC (fixed 3600s steps,
no ambiguity) and derive the local bucket key only at the end — this is exactly what
PriorAnalyzer.calculate_time_priors() does (data/analysis.py:675-803, comment at
:702-704) specifically to avoid the repeated-local-hour ambiguity during fall-back DST.
Any new duration/period calculation: sanity-check the result isn't a suspiciously round
number of hours away from what you'd expect — that's the smoking gun.
When to run this: any bug report mentioning wrong times, negative/huge durations,
"prior looks shifted by X hours", "aggregation happens at the wrong hour", or anything that
reproduces differently for users in different timezones; before merging any new code that reads
raw datetimes from the DB or does datetime arithmetic outside time_utils.py's existing helpers.
Recipe 5 — Statistical sufficiency: is this learned number trustworthy?
What it proves: whether a learned probability/correlation/transition estimate has enough
observations behind it to act on, or whether it's noise dressed up as a number.
The core idea, in one line: as sample count n grows, an estimate's precision improves
roughly as 1/√n, so somewhere below a project-specific floor the noise swamps the signal and
you should fall back to a wider/coarser default rather than trust the specific number. This
integration hard-codes that floor in two structurally identical places:
A. Correlation analysis — MIN_CORRELATION_SAMPLES = 50 (const.py:323,
db/correlation.py:99,1056,1541):
Below 50 samples: correlation isn't even computed (return (0.0, 1.0) — zero strength, p=1).
At/above 50: confidence = min(1.0, abs_correlation × (1 − 50/sample_count)). At exactly 50
samples, confidence is forced to 0 regardless of the raw correlation coefficient; it only
approaches the raw coefficient asymptotically as sample_count → ∞.
Reloading a previously-saved correlation re-checks sample_count < 50 and discounts/ignores
it if the count has since dropped (e.g. after a data purge) below the floor.
defconfidence(abs_corr, n, floor=50):
returnmin(1.0, abs_corr * (1 - floor / n)) if n >= floor else0.0print(confidence(0.8, 50)) # 0.0 -- exactly at the floor, zero trustprint(confidence(0.8, 100)) # 0.4 -- half-discountedprint(confidence(0.8, 1000)) # 0.76 -- close to the raw 0.8
B. Adjacency transition smoothing — the same idea, four thresholds gating six fallback levels
(custom_components/area_occupancy/db/transitions.py:487-651, constants at const.py:216-221
— PR #454, merged to main 2026-07-06): lookup_transition_probability() walks from most-specific to least-specific,
using the first level whose total observation count clears its threshold:
Level
Scope
Threshold constant
Value
1
2-hop chain, exact hour-of-week
ADJACENCY_N_SPECIFIC
5
2
2-hop chain, hour-of-day (weekdays collapsed)
ADJACENCY_N_HOUR
20
3
2-hop chain, un-bucketed
ADJACENCY_N_CHAIN
50
4
1-hop chain, exact hour-of-week
ADJACENCY_N_SPECIFIC
5
5
1-hop chain, un-bucketed
ADJACENCY_N_PAIR
20
6
static default
—
no threshold (0 observations signalled)
This is the same sufficiency principle as correlation's single 50-sample floor, just applied
per-bucket-width instead of globally: a narrower bucket (exact hour-of-week) needs fewer total
observations to be "specific enough to matter" (5) because a false read there is cheap and
gets diluted quickly by the wider fallback; a wider un-bucketed pool needs more (50) before
you'll trust it over falling all the way back to a static default, because there's no narrower
level left to catch an error.
How to judge "is this learned number trustworthy" in general — apply this order of checks:
Count check (hard floor): is sample_count/observed_count/total_count at or above the
relevant constant? If below, don't trust the number at all — it should already be gated out
in code; if you see a raw learned value being used despite a sub-floor count, that's a bug.
Confidence/discount check (soft floor): even above the hard floor, is the discount factor
(e.g. correlation's 1 - 50/n) still heavily attenuating the value? A value with n=52 and a
raw correlation of 0.9 still only carries confidence 0.9 * (1 - 50/52) ≈ 0.035 — technically
"computed" but practically noise.
Variance/spread check: for numeric (Gaussian) correlations, look at
std_occupied/std_unoccupied relative to the gap between mean_occupied and
mean_unoccupied — if the two distributions overlap heavily (means within ~1 std of each
other), no sample count fixes that; use scripts/visualize_distributions.py to plot it
directly rather than trusting correlation_strength blind.
Time-coverage check (priors specifically): calculate_time_priors() tracks
slot_weeks_total (distinct ISO weeks contributing to each of the 168 day-of-week × hour-of-day
buckets) as a diagnostic, but as of 2026-07-06 this is not used to gate whether a slot's
prior is trusted — no minimum-weeks threshold is enforced before a slot prior is written. If
you're investigating a noisy time-prior, check data_points_per_slot yourself; the code
won't stop you from trusting a slot backed by a single week's data.
When to run this: whenever a bug report or PR claims "the learned prior/correlation/adjacency
influence is wrong" — first ask how many samples/observations back it, using the table above,
before assuming the math is broken; before lowering any MIN_*/ADJACENCY_N_* threshold (lowering
it trades stability for responsiveness — quantify the confidence at the new floor using the
formula above); when reviewing a PR that adds a new learned-from-history feature — it needs an
explicit sufficiency floor of its own, not an implicit "well it'll average out."
Recipe 6 — Denominator/period reasoning (auditing any ratio)
What it proves: that a ratio's numerator and denominator cover the same time window —
the general bug class behind issue #483 (Recipe 1), but applicable to any X / Y in the
codebase, not just the global prior.
The failure pattern, generalized: someone narrows (or widens) one side of a ratio for a
legitimate-sounding reason ("guard against a degenerate startup period", "exclude the tail we
don't have data for yet") without symmetrically adjusting the other side. The ratio then silently
answers a different question than its name claims to answer.
Checklist — apply to any ratio you're adding, reviewing, or debugging:
Name the window explicitly. Write down, in plain language, "numerator = X measured
over window W; denominator = Y measured over window W" — the sameW. If you can't
state one shared W, the ratio is already suspect.
Trace both sides to their window-defining variables independently. In #483, the
numerator (occupied_duration) summed intervals over [first_interval_start, now] while
the denominator (actual_period_duration) used [first_interval_start, last_interval_end]
— different right edges. Find the equivalent left/right-edge variables for whatever ratio
you're auditing and diff them.
Ask what happens during a real "boring" period (quiet overnight, weekend, vacation).
Boring periods are exactly when truncation logic tends to kick in ("no new data since
X, so shrink the window") — and they're exactly when a numerator/denominator mismatch
does the most damage, because they run every single hourly cycle, compounding.
Check whether "now" is used consistently. Ratios computed against dt_util.utcnow()
should use one captured now value throughout, not one now() call for the numerator and
a later one for the denominator (races are small here, but the conceptual mismatch of
"which now" is the same bug shape).
Look for an asymmetric guard clause. Grep for if branches near the ratio that adjust
one side's bound "defensively" — e.g. if (now - last_interval_end).total_seconds() > 3600: actual_period_end = last_interval_end (the exact #483 line,
data/analysis.py:517-520) truncates the denominator's end but never touches the
numerator. A defensive clamp on one side without an equal clamp on the other is the
signature to hunt for.
Re-run Recipe 1's style of hand computation with concrete numbers before and after your
change — if you can't produce a two-column "buggy vs correct" table like the one above,
you haven't verified the fix.
Other ratios in this codebase worth this treatment if you touch them: correlation's
abs_correlation × (1 − MIN_CORRELATION_SAMPLES/sample_count) (Recipe 5 — numerator's
abs_correlation and denominator's sample_count must be computed over the same filtered sample
set); information_gain = |pgt − pgf| / max(pgt, pgf, 0.01) (data/entity.py:309-327 — both
pgt/pgf must come from the same correlation run, not one stale and one fresh); the adjacency
silence_score sum (Recipe 7 — every neighbour's lagged probability and transition probability
must be from the same tick).
When to run this: before merging any change that computes a ratio/rate/percentage from two
independently-sourced quantities; whenever a learned value looks systematically biased in one
direction (inflated, deflated, always saturating) rather than just noisy — systematic bias is the
tell of a window mismatch, not randomness; as a mandatory step when reviewing any PR touching
data/analysis.py's period/window calculations or db/correlation.py's confidence math.
Recipe 7 — Feedback-loop analysis for area-to-area couplings
What it proves: that a new coupling between areas (or between an area and its own history)
cannot amplify itself into a runaway loop within a single computation tick.
Why this matters here specifically: the adjacency feature (PR #454, merged to main
2026-07-06) is the first place this integration lets one area's probability influence another's —
it remains unvalidated on real homes, so treat it as a candidate feature under active scrutiny,
not settled behavior. Any coupling of this shape is a feedback-loop risk by
construction: if area A's boost depends on area B's current probability, and area B's boost
depends on area A's current probability, a single tick could see both areas inflate each other
before either settles — worse, over multiple ticks this could compound instead of converge.
How this codebase avoids it — the lagged-snapshot pattern (coordinator.py:526-574update(), :603-652_compute_adjacency_state): every coordinator tick first snapshots the
previous tick's per-area probability/occupied state into self._lagged_probabilities /
was_occupiedbefore computing anything new for the current tick. compute_decay_modifier()'s
silence_score and TrajectoryTracker.observe()'s end-edge detection both read exclusively from
this lagged snapshot — never from an in-flight, still-being-recomputed value. All areas'
adjacency boosts and decay modifiers are precomputed together in one pass
(_compute_adjacency_state) before any area's probability()/half_life is recomputed for the
tick, so no area's own recompute can feed back into its neighbours' inputs within that same tick.
Checklist for auditing any new coupling (between areas, or between an entity and its own
derived state) for self-reinforcement:
Identify the read: what upstream value does the new logic consume (another area's
probability, its own decayed state, a correlation computed earlier in the same pipeline
run)?
Identify the write: what does the new logic produce, and does that output feed back —
directly or via a later pipeline step in the same run — into the value it just read?
Is the read lagged or live? If it's live (this tick's freshly-computed value), you have
a same-tick feedback risk. Change it to read the previous tick/previous pipeline-step's
snapshot instead, mirroring the _lagged_probabilities pattern.
Is there a cap on the output regardless of input? Even with lagging, an unbounded gain
could let cross-tick oscillation grow slowly. Check for an explicit ceiling — the decay
modifier's cap = ADJACENCY_DECAY_MODIFIER_MAX = 1.75 (const.py:214) and the boost path's
implicit ceiling (logit-space additions saturate through sigmoid/clamp_probability at
[0.01, 0.99], so no additive boost can push a probability outside that band — see Recipe
2) are the two examples here. A new coupling without an analogous cap is under-specified.
Simulate two ticks by hand: pick two coupled areas, assign starting probabilities, run
one tick of your new formula using "tick 0" values for both, then run "tick 1" using the
lagged "tick 0" outputs. Confirm the values move toward a fixed point, not away from one. If
you're not sure how to compute a fixed point analytically, at least confirm empirically
(in a quick Python script or the simulator/ Flask app) that 10+ synthetic ticks converge
rather than diverge or oscillate with growing amplitude.
Check the recency-decay term for transition counts if the coupling learns from history:
ADJACENCY_RECENCY_HALF_LIFE_DAYS = 30 (const.py:198) exponentially decays old transition
counts each pipeline run before adding new ones — this is what keeps the learned influence
adapting to current patterns rather than accumulating unboundedly forever. Any new
learned-history coupling needs an equivalent recency mechanism or it will ossify around
whatever pattern existed when it first accumulated enough samples.
When to run this: before merging any PR that lets one area read another area's state
(current or historical); before merging any coupling between a value and its own past output
(e.g. a rolling average, an exponential smoother); whenever a bug report describes probability
"oscillating," "climbing without bound," or "two rooms bouncing off each other"; as a mandatory
design-review step for any future extension of the adjacency feature (multi-hop chains beyond
2-hop, weighted multi-neighbour boosts, etc.) or any other cross-area feature the roadmap adds
(e.g. "Occupancy Zone Hierarchies" from the README's Planned Features).
Provenance and maintenance
Date-stamped: 2026-07-06, integration version 2026.5.17 (custom_components/area_occupancy/manifest.json:20,
pyproject.toml:7, const.py:32 as DEVICE_SW_VERSION — no tagged release yet carries today's
merge wave; this is still main's HEAD version). All facts in this skill were verified directly
against the repository at main HEAD (17b71d2, post-merge-wave of 2026-07-06) unless marked
"unverified."
Adjacency-feature facts (Recipes 2, 5-table-B, 7) describe code that merged to main via PR #454
on 2026-07-06 (#456 closed as merged into it). The feature is complete on main but remains
unvalidated on real homes — still a candidate for future tuning, not settled behavior.
Re-verify current state before relying on exact file paths:
