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
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