Hook up a dataset importer for training the AIs, but it's untested.

Also, we don't have any code that actually does the training itself 
either yet.

server/Helpers/Math.mjs

@@ -0,0 +1,16 @@
+"use strict";
+
+function normalise(value, { min : input_min, max: input_max }, { min : output_min, max: output_max }) {
+	return (
+		((value - input_min) / (input_max - input_min)) * (output_max - output_min)
+	) + output_min
+}
+
+function clamp(value, min, max) {
+	if(value > max) return max;
+	if(value < min) return min;
+	return value;
+}
+
+
+export { normalise, clamp };

server/Repos.SQLite/RSSIRepo.mjs

@@ -23,6 +23,18 @@ class RSSIRepo {
 		}
 	}
 	
+	iterate_gateway(gateway_id) {
+		return this.db.prepare(`SELECT
+				rssis.*,
+				readings.latitude,
+				readings.longitude
+			FROM rssis
+			JOIN readings ON rssis.gateway_id = readings.id
+			WHERE gateway_id = :gateway_id`).iterate({
+				gateway_id
+			});
+	}
+	
 	iterate() {
 		return this.db.prepare(`SELECT * FROM rssis`).iterate();
 	}

server/bootstrap/container.mjs

@@ -13,6 +13,9 @@ import MessageHandler from '../ttn-app-server/MessageHandler.mjs';
 
 import DataProcessor from '../process-data/DataProcessor.mjs';
 
+import AITrainer from '../train-ai/AITrainer.mjs';
+import DatasetFetcher from '../train-ai/DatasetFetcher.mjs';
+
 import settings from './settings.mjs';
 import database_init from '../bootstrap/database_init.mjs';
 
@@ -31,7 +34,11 @@ export default async function setup() {
 		database: a.asFunction(database_init).singleton(),
 		TTNAppServer: a.asClass(TTNAppServer),
 		MessageHandler: a.asClass(MessageHandler),
-		DataProcessor: a.asClass(DataProcessor)
+		
+		DataProcessor: a.asClass(DataProcessor),
+		
+		AITrainer: a.asClass(AITrainer),
+		DatasetTrainer: a.asClass(DatasetTrainer)
 	});
 	
 	// Enable / disable colourising the output

server/settings.default.toml

@@ -54,6 +54,11 @@ devices = [
 [ai]
 # Settings relating to the training of the AI. Note that a number of these settings can also be specified by environment variables, to aid with fiddling with the parameters to find the right settings.
 
+# Min / max dataset values when training the AI, since neural networks only take values between 0 and 1.
+# Note that changing these means that you've got to retrain the AIs all over again!
+rssi_min = -150
+rssi_max = 0
+
 [logging]
 # The format the date displayed when logging things should take.
 # Allowed values: relative (e.g like when a Linux machine boots), absolute (e.g. like Nginx server logs), none (omits it entirely))

server/train-ai/AITrainer.mjs

@@ -3,8 +3,10 @@
 import tf from '@tensorflow/tfjs-node-gpu';
 
 class AITrainer {
-	constructor({ settings }) {
+	constructor({ settings, GatewayRepo, DatasetFetcher }) {
 		this.settings = settings;
+		this.dataset_fetcher = DatasetFetcher;
+		this.repo_gateway = GatewayRepo;
 		this.model = this.generate_model();
 	}
 	
@@ -29,8 +31,15 @@ class AITrainer {
 		return model;
 	}
 	
-	train() {
-		
+	async train() {
+		for(let gateway of this.repo_gateway.iterate()) {
+			let dataset = this.dataset_fetcher.fetch(gateway.id);
+			await this.train_dataset(dataset);
+		}
+	}
+	
+	async train_dataset(dataset) {
+		// TODO: Fill this in
 	}
 }
 

server/train-ai/DatasetFetcher.mjs

@@ -0,0 +1,37 @@
+"use strict";
+
+import { normalise, clamp } from '../Helpers/Math.mjs';
+
+class DatasetFetcher {
+	constructor({ settings, RSSIRepo }) {
+		this.settings = settings;
+		this.repo_rssi = RSSIRepo;
+	}
+	
+	fetch(gateway_id) {
+		let result = [];
+		for(let rssi of this.repo_rssi.iterate_gateway(gateway_id) {
+			result.push({
+				input: [
+					normalise(rssi.latitude,
+						{ min: -90, max: +90 },
+						{ min: 0, max: 1 }
+					),
+					normalise(rssi.longitude,
+						{ min: -180, max: +180 },
+						{ min: 0, max: 1 }
+					)
+				],
+				output: [
+					clamp(normalise(rssis.rssi,
+						{ min: this.settings.ai.rssi_min, max: this.settings.ai.rssi_max },
+						{ min: 0, max: 1 }
+					), 0, 1)
+				]
+			});
+		}
+		return result;
+	}
+}
+
+export default DatasetFetcher;