Codex: publish bayesian room probabilities

README.md

@@ -9,6 +9,7 @@ A Home Assistant Add-on / Docker container that solves indoor positions using MQ
 - Manages and configures ESPresense nodes
 - Updates node firmware
 - Adjusts device-specific settings
+- Publishes Bayesian room probabilities for fuzzy automations
 - Monitors and controls automatic node optimization
 
 ![image](https://user-images.githubusercontent.com/1491145/208942192-d8716e50-c822-48a7-a6d3-46b53ab9373e.png)
@@ -19,6 +20,42 @@ A Home Assistant Add-on / Docker container that solves indoor positions using MQ
 3. [Node Setup](https://espresense.com/companion/configuration#node-placement)
 4. [Optimization Guide](https://espresense.com/companion/optimization)
 
+## Bayesian probability output
+
+Enable the optional Bayesian publisher to expose per-room probability vectors alongside the traditional `device_tracker` state.
+Set `bayesian_probabilities.enabled: true` in `config.yaml` to turn it on:
+
+```yaml
+bayesian_probabilities:
+  enabled: true
+  discovery_threshold: 0.1   # auto-create sensors above this probability
+  retain: true               # keep MQTT state so HA restores after restart
+```
+
+When enabled the companion:
+
+- Publishes `espresense/companion/<device_id>/probabilities/<room>` topics containing a `0.0-1.0` float for each room.
+- Adds a `probabilities` object to the device attribute payload (`espresense/companion/<device_id>/attributes`).
+- Auto-discovers Home Assistant `sensor` entities for any room whose probability crosses the configured threshold.
+
+You can fuse multiple device probabilities into a person-level Bayesian sensor in Home Assistant:
+
+```yaml
+sensor:
+  - platform: bayesian
+    name: "Pat in Kitchen"
+    prior: 0.5
+    observations:
+      - platform: template
+        value_template: "{{ states('sensor.pat_phone_kitchen_probability') | float }}"
+        probability: 0.6
+      - platform: template
+        value_template: "{{ states('sensor.pat_watch_kitchen_probability') | float }}"
+        probability: 0.9
+```
+
+Automations can then trigger on thresholds (for example, turn on lights when `sensor.pat_in_kitchen > 0.7`).
+
 ## Need Help?
 - Join our [Discord Community](https://discord.gg/jbqmn7V6n6)
 - Check the [Troubleshooting Guide](https://espresense.com/companion/troubleshooting)

src/Models/AutoDiscovery.cs

@@ -119,6 +119,16 @@ public class DiscoveryRecord
 
         [JsonProperty("status_topic")] public string? EntityStatusTopic { get; set; }
 
+        [JsonProperty("device_class")] public string? DeviceClass { get; set; }
+
+        [JsonProperty("state_class")] public string? StateClass { get; set; }
+
+        [JsonProperty("unit_of_measurement")] public string? UnitOfMeasurement { get; set; }
+
+        [JsonProperty("value_template")] public string? ValueTemplate { get; set; }
+
+        [JsonProperty("icon")] public string? Icon { get; set; }
+
         [JsonProperty("device")] public DeviceRecord? Device { get; set; }
 
         [JsonProperty("origin")] public OriginRecord? Origin { get; set; }

src/Models/Config.Clone.cs

@@ -12,7 +12,7 @@
         {
             return new Config
             {
                 Mqtt = Mqtt?.Clone(),
                 Bounds = Bounds?.Select(b => b.ToArray()).ToArray(),
                 Timeout = Timeout,
                 AwayTimeout = AwayTimeout,
@@ -24,7 +24,8 @@
                 ExcludeDevices = ExcludeDevices.Select(d => d.Clone()).ToArray(),
                 History = History.Clone(),
                 Locators = Locators.Clone(),
-                Optimization = Optimization.Clone()
+                Optimization = Optimization.Clone(),
+                BayesianProbabilities = BayesianProbabilities.Clone()
             };
         }
     }
@@ -116,6 +117,19 @@
         }
     }
 
+    public partial class ConfigBayesianProbabilities
+    {
+        public ConfigBayesianProbabilities Clone()
+        {
+            return new ConfigBayesianProbabilities
+            {
+                Enabled = Enabled,
+                DiscoveryThreshold = DiscoveryThreshold,
+                Retain = Retain
+            };
+        }
+    }
+
     public partial class ConfigHistory
     {
         public ConfigHistory Clone()

src/Models/Config.cs

@@ -41,6 +41,9 @@ public partial class Config
         [YamlMember(Alias = "history")]
         public ConfigHistory History { get; set; } = new();
 
+        [YamlMember(Alias = "bayesian_probabilities")]
+        public ConfigBayesianProbabilities BayesianProbabilities { get; set; } = new();
+
         [YamlMember(Alias = "bounds")]
         public double[][] Bounds { get; set; } = [];
 
@@ -143,6 +146,18 @@ public partial class ConfigOptimization
         [YamlIgnore] public double RmseWeight => Weights.TryGetValue("rmse", out var val) ? val : 0.5;
     }
 
+    public partial class ConfigBayesianProbabilities
+    {
+        [YamlMember(Alias = "enabled")]
+        public bool Enabled { get; set; } = false;
+
+        [YamlMember(Alias = "discovery_threshold")]
+        public double DiscoveryThreshold { get; set; } = 0.1;
+
+        [YamlMember(Alias = "retain")]
+        public bool Retain { get; set; } = true;
+    }
+
     public partial class ConfigHistory
     {
         [YamlMember(Alias = "enabled")] public bool Enabled { get; set; } = false;

src/Models/Device.cs

@@ -66,7 +66,7 @@ public DateTime? LastSeen
     {
         get
         {
-            var lastSeen =  BestScenario?.LastHit ?? Nodes.Values.Max(a => a.LastHit);
+            var lastSeen = BestScenario?.LastHit ?? Nodes.Values.Max(a => a.LastHit);
             if (_lastSeen == null || lastSeen > _lastSeen) _lastSeen = lastSeen;
             return _lastSeen;
         }
@@ -82,6 +82,12 @@ public DateTime? LastSeen
     [STJ.JsonIgnore] public Scenario? BestScenario { get; set; }
     [STJ.JsonIgnore] public IList<Scenario> Scenarios { get; } = new List<Scenario>();
 
+    [STJ.JsonIgnore]
+    public ConcurrentDictionary<string, double> BayesianProbabilities { get; } = new(StringComparer.OrdinalIgnoreCase);
+
+    [STJ.JsonIgnore]
+    public ConcurrentDictionary<string, AutoDiscovery> BayesianDiscoveries { get; } = new(StringComparer.OrdinalIgnoreCase);
+
     [STJ.JsonConverter(typeof(Point3DConverter))]
     public Point3D? Location => Anchor?.Location ?? (BestScenario == null ? null : _kalmanLocation.Location);
 
@@ -113,7 +119,7 @@ public double? MeasuredRefRssi
         {
             var currentNodes = Nodes.Values.Where(dn => dn.Current).ToList();
             if (currentNodes.Count == 0) return null;
-            
+
             var refRssiValues = currentNodes.Where(dn => dn.RefRssi != 0).Select(dn => dn.RefRssi).ToList();
             return refRssiValues.Count > 0 ? refRssiValues.Average() : null;
         }
@@ -143,6 +149,17 @@ public void SetAnchor(DeviceAnchor? anchor)
         }
     }
 
+    public void ResetBayesianState()
+    {
+        foreach (var discovery in BayesianDiscoveries.Values.ToList())
+        {
+            HassAutoDiscovery.Remove(discovery);
+        }
+
+        BayesianDiscoveries.Clear();
+        BayesianProbabilities.Clear();
+    }
+
     public virtual IEnumerable<KeyValuePair<string, string>> GetDetails()
     {
         yield return new KeyValuePair<string, string>("Best Scenario", $"{BestScenario?.Name}");

src/Services/DeviceTracker.cs

@@ -243,6 +243,7 @@ private async Task<bool> CheckDeviceAsync(Device device)
                 Log.Information("[-] Track {Device}", device);
                 foreach (var ad in device.HassAutoDiscovery)
                     await ad.Delete(mqtt);
+                device.ResetBayesianState();
             }
             return true;
         }