Use new tidied normalising code on the client-side

client_src/js/Worker/AIWrapper.mjs

@@ -5,7 +5,19 @@ import path from 'path';
 import brain from 'brain.js';
 import haversine from 'haversine-distance';
 
-import { normalise, clamp } from '../../../common/Math.mjs';
+import {
+	normalise_lat,
+	normalise_lng,
+	unnormalise_lat,
+	unnormalise_lng,
+	
+	normalise_gateway_distance,
+	unnormalise_gateway_distance,
+	
+	normalise_rssi,
+	unnormalise_rssi
+} from '../../../common/Normalisers.mjs';
+
 import GetFromUrl from '../Helpers/GetFromUrl.mjs';
 
 class AIWrapper {
@@ -62,34 +74,20 @@ class AIWrapper {
 					{ latitude: lat, longitude: lng },
 					this.gateways.get(gateway_id)
 				);
+				
 				max_predicted_rssi = Math.max(
 					max_predicted_rssi,
 					ai.run({
-						latitude: normalise(lat,
+						latitude: normalise_lat(lat),
-							{ min: -90, max: +90 },
+						longitude: normalise_lng(lng),
-							{ min: 0, max: 1 }
+						distance: normalise_gateway_distance(
+							distance_from_gateway
 						),
-						longitude: normalise(lng,
+					})[0]
-							{ min: -180, max: +180 },
-							{ min: 0, max: 1 }
-						),
-						distance: clamp(
-							normalise(distance_from_gateway,
-								{ min: 0, max: 20000 },
-								{ min: 0, max: 1 }
-							),
-						0, 1)
-					})
 				);
 			}
 			
-			max_predicted_rssi = normalise(max_predicted_rssi,
+			max_predicted_rssi = unnormalise_rssi(max_predicted_rssi);
-				{ min: 0, max: 1 },
-				{
-					min: this.index.properties.rssi_min,
-					max: this.index.properties.rssi_max
-				}
-			);
 			
 			if(max_predicted_rssi > stats.rssi_max)
 				stats.rssi_max = max_predicted_rssi;

server/train-ai/AITrainer.mjs

@@ -55,8 +55,7 @@ class AITrainer {
 			),
 			JSON.stringify({
 				properties: {
-					rssi_min: this.settings.ai.rssi_min,
+					
-					rssi_max:  this.settings.ai.rssi_max
 				},
 				index
 			})