Confirmed: learned models were matched only by zone key, so replacing a
zone's sensors, changing sensor weights, moving or replacing a heat
source or turning a window kept coefficients learned for a different
configuration, silently.
compatibility.py fingerprints exactly what each learned component
depends on: the zone measurement (sensors and relative weights; equal
scaling changes nothing), each zone's heat sources, the neighbour's
measurement, the zone's windows, window orientation (direction term
only), the recovery time (recovery only) and other zones' openings
(cross term only). Comfort weight, target offset and names are not
included. Heat-source baselines have their own fingerprint (sensor and
included sources).
On restore, a changed measurement recalibrates the zone: base and
extended loss/heat/bias keep their values as a warm start, but their
uncertainty, counts and readiness start over, other terms return to the
prior, and the zone's forecast statistics are dropped. Otherwise only
the changed terms are reset; everything else, including other zones,
is kept. Each recalibration is logged (Status model_changes, info log)
and saved at once. Data from before fingerprints (0.4.0) is assumed to
match its current configuration and is fingerprinted at the first
start, so the next edit is detected even before a learned sample.
Tests cover comfort changes, sensor replacement and weights, window
orientation and size, moved and replaced heat sources, re-added zones,
0.4.0 data and a second restart, plus an HA Core test through real
subentry updates.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Electrical heat, ventilation, wind and inter-zone exchange each need
their own coefficient next to the existing loss/heat/bias, and each must
stay physically bounded and not claim precision it does not have.
learning.py adds FeatureLearner: exponentially weighted RLS over named
features with per-feature bounds, priors at 0 for new effects, the same
covariance windup bound as the base model, and a linked bound that keeps
the directional wind term from cancelling more than the isotropic one.
Each prior is sized as "worth N typical samples" (20 for new effects,
200 for zone coupling, which is weakly identifiable). Confidence is the
share of a coefficient's information that comes from data; the standard
error uses a bias-corrected running noise estimate. Saved models are
matched by feature name, so adding a heat source, window or neighbour
retrains only that coefficient and keeps the covariance of the rest.
The existing 3-parameter learner now uses the same RLS step, covariance
bound and positive-definiteness check (behavior unchanged; all existing
tests pass).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
buzeraandClaude Opus 5.5