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>

tests/test_physics.py

@@ -140,6 +140,31 @@ class ModelTests(unittest.TestCase):
         self.assertEqual(restored.count, 30000)
         self.assertEqual(restored.p, learn.p)
 
+    def test_confidence_reflects_excitation_not_sample_count(self):
+        # Two weeks of samples, relay always off, a steady 15 degC difference to outside.
+        steady = m.Learner()
+        for _ in range(4000):
+            steady.learn(20.0, 20.0 + 0.003 * -15 + 0.001, 5.0, 0.0, command=False)
+        confidence = steady.confidence()
+        self.assertEqual(steady.count, 4000)
+        self.assertEqual(confidence['heat'], 0)           # never exercised
+        self.assertLess(confidence['loss'], 0.8)          # not separable from bias
+        self.assertLess(confidence['bias'], 0.8)
+        # Fewer samples, with the relay cycling and a cold and a mild spell each day.
+        varied = m.Learner()
+        t, lag = 20.0, 0.0
+        for step in range(1200):
+            heater = int(step % 96 < 35)
+            lag = lag + m.HEAT_LAG * (heater - lag)
+            outside = -2 + 14 * ((step // 144) % 2)
+            value = t + 0.003 * (outside - t) + 0.055 * lag + 0.001
+            varied.learn(t, value, outside, lag, command=bool(heater))
+            t = value
+        self.assertTrue(all(value > 0.8 for value in varied.confidence().values()), varied.confidence())
+        # Recalibration starts confidence over.
+        varied.recalibrate()
+        self.assertEqual(set(varied.confidence().values()), {0})
+
     def test_restore_tolerates_round_off_and_bounds_wound_up_covariance(self):
         # Shape of a v0.2.1 model after a summer: huge heat variance, tiny asymmetry.
         data = m.Learner(count=40000, off_count=40000).as_dict()