LoRaWAN-Signal-Mapping/client_src/js/Worker/AIWrapper.mjs

"use strict";


import brain from 'brain.js';
import haversine from 'haversine-distance';

import {
	normalise_lat, normalise_lng,
	
	normalise_gateway_distance,
	
	unnormalise_rssi
} from '../../../common/Normalisers.mjs';


class AIWrapper {
	constructor() {
		this.setup_complete = false;
		
		this.map_bounds = null;
		this.index = null;
		this.Config = null;
		
		this.gateways = new Map();
	}
	
	async setup({ bounds, index, Config }) {
		this.map_bounds = bounds;
		this.index = index;
		this.Config = Config;
		
		console.log("Loading models");
		
		// WebGL isn't available inside WebWorkers yet :-(
		
		for(let gateway of this.index.index) {
			let net = new brain.NeuralNetwork(/*gateway.net_settings*/);
			net.fromJSON(
				gateway.frozen_net
			);
			
			this.gateways.set(
				gateway.id,
				{ ai: net, latitude: gateway.latitude, longitude: gateway.longitude }
			);
		}
		console.log("Model setup complete.");
		this.setup_complete = true;
	}
	
	predict_row(lat) {
		if(!this.setup_complete)
			throw new Error("Error: Can't do predictions until the setup is complete.");
			
		let result = [],
			stats = {
				rssi_min: Infinity,
				rssi_max: -Infinity
			};
		
		for(let lng = this.map_bounds.west; lng < this.map_bounds.east; lng += this.Config.step.lng) {
			let max_predicted_rssi = -Infinity;
			
			for(let [gateway_id, gateway] of this.gateways) {
				let distance_from_gateway = haversine(
					{ latitude: lat, longitude: lng },
					gateway
				);
				let next_value = gateway.ai.run({
					latitude: normalise_lat(lat),
					longitude: normalise_lng(lng),
					distance: normalise_gateway_distance(
						distance_from_gateway
					),
				});
				if(isNaN(next_value[0])) {
					console.log(next_value);
					throw new Error("Error: Neural Network returned NaN");
				}
				
				// console.log(next_value);
				max_predicted_rssi = Math.max(
					max_predicted_rssi,
					next_value[0]
				);
			}
			let chance = Math.random() < 0.01;
			if(chance) console.log(max_predicted_rssi);
			max_predicted_rssi = unnormalise_rssi(max_predicted_rssi);
			if(chance) console.log(max_predicted_rssi);
			
			if(max_predicted_rssi > stats.rssi_max)
				stats.rssi_max = max_predicted_rssi;
			if(max_predicted_rssi < stats.rssi_min)
				stats.rssi_min = max_predicted_rssi;
			
			result.push(max_predicted_rssi);
		}
		
		return { result, stats };
	}
}

export default AIWrapper;