Report how well the base coefficients are identified

The base layer (loss, heat, bias) showed values only, so a zone with weeks of
samples looked equally trustworthy whether or not the data ever varied its
inputs. Each base coefficient now reports a confidence from its own posterior
variance, as the extended and ventilation layers already did: it stays at 0
for `heat` while the relay never cycles and below 0.8 for `loss` and `bias`
while the indoor-outdoor difference is steady. Readiness is unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

custom_components/heating_mpc/physics.py

@@ -134,6 +134,17 @@ class Learner:
     def mae(self) -> float | None:
         return self.error_sum / self.count if self.count else None
 
+    def confidence(self) -> dict[str, float]:
+        """Per coefficient, the share of its information that comes from data (0 = prior only).
+
+        Taken from the coefficient's own uncertainty, not from the sample count: it
+        only grows while the data varies that coefficient's input independently of
+        the others. Weeks with the relay always off leave `heat` at 0; a steady
+        indoor-outdoor difference cannot separate `loss` from `bias`.
+        """
+        return {name: round(1 - min(prior, max(0.0, self.p[i][i])) / prior, 3)
+                for i, (name, prior) in enumerate(zip(("loss", "heat", "bias"), PRIOR_VARIANCE))}
+
     def as_dict(self) -> dict[str, Any]:
         return {"theta": self.theta, "p": self.p, "count": self.count, "error_sum": self.error_sum,
                 "on_count": self.on_count, "off_count": self.off_count,