Show learning activity with Learning Progress and Last Sample sensors

During a first test the Status entity only said "learning" for the
first half hour (the radiator-lag wait after a restart), and the
attributes explaining why were hard to find. Add two sensors:

- Learning Progress (%): for the least trained room, the mean of its
  three readiness requirements (200 samples, 30 ON, 30 OFF), each capped
  at 100 %; 100 % means ready. Attributes show the counts.
- Last Sample: an enum of the last poll's learning outcome, with English
  and Romanian state names (e.g. "Waiting for radiators to settle"). Its
  history shows when learning happens.

Polls blocked by missing shared inputs now report waiting_for_inputs.
Readiness thresholds move to const.py.

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

custom_components/heating_mpc/coordinator.py

@@ -17,7 +17,7 @@ from .const import (
     CONF_ROOM_MAX_AGE, CONF_ROOM_STALE_KEEP, CONF_WEATHER_MAX_AGE, ROOM_STALE_KEEP_MAX_MINUTES,
     DOMAIN, CONF_CLIMATE, CONF_HEATER, CONF_ROOMS, CONF_WEIGHTS, WEATHER_SOURCES,
     CONF_HORIZON, CONF_MIN_ON, CONF_MIN_OFF, CONF_SWITCH_COST, CONF_HEAT_COST,
-    CONF_BAND, DEFAULTS,
+    CONF_BAND, DEFAULTS, READY_OFF_SAMPLES, READY_ON_SAMPLES, READY_SAMPLES,
 )
 from .data_quality import ReportAgeHistory, freshness
 from .evaluation import ForecastEvaluator
@@ -159,6 +159,17 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self._relay_events_dropped = False
         return steady
 
+    def _count_result(self, status: dict[str, Any], result: str) -> None:
+        """Record how this poll was handled for learning, and the learning progress."""
+        status["sample_result"] = result
+        self._sample_results[result] = self._sample_results.get(result, 0) + 1
+        status["sample_results_since_start"] = dict(self._sample_results)
+        # Least trained room: mean of its three readiness requirements (100 = ready).
+        status["learning_progress"] = round(100 * min(
+            (min(1.0, m.count / READY_SAMPLES) + min(1.0, m.on_count / READY_ON_SAMPLES)
+             + min(1.0, m.off_count / READY_OFF_SAMPLES)) / 3
+            for m in self.learners.values()), 1)
+
     def _prediction_inputs(self) -> tuple:
         """Values the prediction depends on, used to detect changes during calculation."""
         heater = self.config[CONF_HEATER]
@@ -260,6 +271,7 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             self._previous = None
             self._evaluator.reset_window()
             status["forecast_accuracy"] = self._evaluator.summary(self.rooms)
+            self._count_result(status, "waiting_for_inputs")
             return status
 
         # A room learns only across a poll pair where it was usable at both ends.
@@ -290,9 +302,7 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                 self._update_counts()
                 learned = True
 
-        result = status["sample_result"]
-        self._sample_results[result] = self._sample_results.get(result, 0) + 1
-        status["sample_results_since_start"] = dict(self._sample_results)
+        self._count_result(status, status["sample_result"])
         self._previous = {
             "time": now, "temps": vals, "outside": outside, "heater_on": heater_on,
             "outside_source": active_sources["temperature"],
@@ -359,8 +369,9 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         # Do not act on a cold model: every room used needs ~17 hours of samples
         # plus both relay conditions.
         used_models = [self.learners[room] for room in vals]
-        if (min(m.count for m in used_models) < 200 or min(m.on_count for m in used_models) < 30
-                or min(m.off_count for m in used_models) < 30):
+        if (min(m.count for m in used_models) < READY_SAMPLES
+                or min(m.on_count for m in used_models) < READY_ON_SAMPLES
+                or min(m.off_count for m in used_models) < READY_OFF_SAMPLES):
             status["status"] = "learning"
             return status
         try: