Finish refactoring to use Brain.js, but it's untested.

client_src/js/Worker/AIWrapper.mjs

@@ -2,13 +2,11 @@
 
 import path from 'path';
 
-import {
+import brain from 'brain.js';
-	loadLayersModel as tf_loadLayersModel,
+import haversine from 'haversine-distance';
-	tensor as tf_tensor,
-	setBackend as tf_setBackend
-} from '@tensorflow/tfjs';
 
-import { normalise } from '../../../common/Math.mjs';
+import { normalise, clamp } from '../../../common/Math.mjs';
+import GetFromUrl from '../Helpers/GetFromUrl.mjs';
 
 class AIWrapper {
 	constructor() {
@@ -29,12 +27,16 @@ class AIWrapper {
 		console.log("Loading models");
 		
 		// WebGL isn't available inside WebWorkers yet :-(
-		tf_setBackend("cpu");
 		
 		for(let gateway of this.index.index) {
+			let net = new brain.NeuralNetwork();
+			net.fromJSON(
+				await GetFromUrl(`${path.dirname(self.location.href)}/${path.dirname(this.Config.ai_index_file)}/${gateway.filename}`)
+			);
+			
 			this.gateways.set(
 				gateway.id,
-				await tf_loadLayersModel(`${path.dirname(self.location.href)}/${path.dirname(this.Config.ai_index_file)}/${gateway.id}/model.json`)
+				net
 			);
 		}
 		console.log("Model setup complete.");
@@ -54,13 +56,29 @@ class AIWrapper {
 		for(let lng = this.map_bounds.west; lng < this.map_bounds.east; lng += this.Config.step.lng) {
 			let max_predicted_rssi = -Infinity;
 			
-			for(let [, ai] of this.gateways) {
+			for(let [gateway_id, ai] of this.gateways) {
-				let next_prediction = ai.predict(
+				let distance_from_gateway = haversine(
-					tf_tensor([ lat, lng ], [1, 2])
+					{ latitude: lat, longitude: lng },
-				).arraySync()[0][0];
+					this.gateways.get(gateway_id)
+				);
 				max_predicted_rssi = Math.max(
 					max_predicted_rssi,
-					next_prediction
+					ai.run({
+						latitude: normalise(lat,
+							{ min: -90, max: +90 },
+							{ min: 0, max: 1 }
+						),
+						longitude: normalise(lng,
+							{ min: -180, max: +180 },
+							{ min: 0, max: 1 }
+						),
+						distance: clamp(
+							normalise(distance_from_gateway,
+								{ min: 0, max: 20000 },
+								{ min: 0, max: 1 }
+							),
+						0, 1)
+					})
 				);
 			}
 			

server/train-ai/AITrainer.mjs

@@ -28,19 +28,20 @@ class AITrainer {
 	async train_all() {
 		let index = [];
 		for(let gateway of this.repo_gateway.iterate()) {
-			let filename = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}`);
+			let filepath = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}.json`);
-			console.log(filename);
+			console.log(filepath);
 			
-			if(!fs.existsSync(path.dirname(filename)))
+			if(!fs.existsSync(path.dirname(filepath)))
-				await fs.promises.mkdir(path.dirname(filename), { recursive: true });
+				await fs.promises.mkdir(path.dirname(filepath), { recursive: true });
 			
-			if(!await this.train_gateway(gateway.id, filename)) {
+			if(!await this.train_gateway(gateway.id, filepath)) {
 				this.l.warn(`Warning: Failed to train AI for ${gateway.id}.`);
 				continue;
 			}
 			
 			index.push({
 				id: gateway.id,
+				filename: path.basename(filepath),
 				latitude: gateway.latitude,
 				longitude: gateway.longitude
 			});
@@ -72,22 +73,9 @@ class AITrainer {
 		this.l.log(`${this.a.fgreen}${this.a.hicol}Training AI for gateway ${gateway_id}${this.a.reset}`);
 		let net = this.generate_neural_net();
 		
-		// let dataset_input = tf.data.generator(
-		// 	this.dataset_fetcher.fetch_input.bind(this.dataset_fetcher, gateway_id)
-		// );
-		// let dataset_output = tf.data.generator(
-		// 	this.dataset_fetcher.fetch_output.bind(this.dataset_fetcher, gateway_id)
-		// );
-		// 
-		// let dataset = tf.data.zip({
-		// 		xs: dataset_input,
-		// 		ys: dataset_output
-		// 	}).shuffle(this.settings.ai.batch_size * 4)
-		// 	.batch(this.settings.ai.batch_size);
-		// 
 		let dataset = this.dataset_fetcher.fetch_all(gateway_id);
 		
-		let result = net.train(dataset, {
+		await net.trainAsync(dataset, {
 			iterations: this.settings.ai.epochs,
 			errorThresh: this.settings.ai.error_threshold,
 			
@@ -97,7 +85,7 @@ class AITrainer {
 			timeout: Infinity
 		});
 		
-		await model.save(`file://${destination_filename}`);
+		await fs.promises.writeFile(destination_filename, net.toJSON());
 		// console.log(result);
 		
 		return true;

server/train-ai/DatasetFetcher.mjs

@@ -13,16 +13,16 @@ class DatasetFetcher {
 	}
 	
 	normalise_latlng(lat, lng) {
-		return [
+		return {
-			normalise(lat,
+			latitude: normalise(lat,
 				{ min: -90, max: +90 },
 				{ min: 0, max: 1 }
 			),
-			normalise(lng,
+			longitude: normalise(lng,
 				{ min: -180, max: +180 },
 				{ min: 0, max: 1 }
 			)
-		];
+		};
 	}
 	
 	fetch_all(gateway_id) {
@@ -33,12 +33,12 @@ class DatasetFetcher {
 			let next_input = this.normalise_latlng(rssi.latitude, rssi.longitude);
 			let distance_from_gateway = haversine(gateway_location, rssi);
 			
-			next_input.push(clamp(
+			next_input.distance = clamp(
 				normalise(distance_from_gateway,
 					{ min: 0, max: 20000 },
 					{ min: 0, max: 1 }
 				),
-			0, 1))
+			0, 1);
 			
 			result.input.push(next_input);