107 lines
2.4 KiB
JavaScript
107 lines
2.4 KiB
JavaScript
"use strict";
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import path from 'path';
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import brain from 'brain.js';
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import haversine from 'haversine-distance';
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import { normalise, clamp } from '../../../common/Math.mjs';
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import GetFromUrl from '../Helpers/GetFromUrl.mjs';
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class AIWrapper {
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constructor() {
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this.setup_complete = false;
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this.map_bounds = null;
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this.index = null;
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this.Config = null;
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this.gateways = new Map();
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}
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async setup({ bounds, index, Config }) {
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this.map_bounds = bounds;
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this.index = index;
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this.Config = Config;
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console.log("Loading models");
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// WebGL isn't available inside WebWorkers yet :-(
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for(let gateway of this.index.index) {
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let net = new brain.NeuralNetwork();
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net.fromJSON(
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// TODO: Move this to the UI thread & do it only once?
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await GetFromUrl(`${path.dirname(self.location.href)}/${path.dirname(this.Config.ai_index_file)}/${gateway.filename}`)
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);
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this.gateways.set(
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gateway.id,
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net
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);
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}
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console.log("Model setup complete.");
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this.setup_complete = true;
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}
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predict_row(lat) {
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if(!this.setup_complete)
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throw new Error("Error: Can't do predictions until the setup is complete.");
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let result = [],
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stats = {
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rssi_min: Infinity,
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rssi_max: -Infinity
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};
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for(let lng = this.map_bounds.west; lng < this.map_bounds.east; lng += this.Config.step.lng) {
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let max_predicted_rssi = -Infinity;
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for(let [gateway_id, ai] of this.gateways) {
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let distance_from_gateway = haversine(
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{ latitude: lat, longitude: lng },
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this.gateways.get(gateway_id)
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);
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max_predicted_rssi = Math.max(
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max_predicted_rssi,
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ai.run({
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latitude: normalise(lat,
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{ min: -90, max: +90 },
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{ min: 0, max: 1 }
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),
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longitude: normalise(lng,
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{ min: -180, max: +180 },
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{ min: 0, max: 1 }
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),
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distance: clamp(
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normalise(distance_from_gateway,
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{ min: 0, max: 20000 },
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{ min: 0, max: 1 }
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),
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0, 1)
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})
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);
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}
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max_predicted_rssi = normalise(max_predicted_rssi,
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{ min: 0, max: 1 },
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{
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min: this.index.properties.rssi_min,
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max: this.index.properties.rssi_max
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}
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);
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if(max_predicted_rssi > stats.rssi_max)
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stats.rssi_max = max_predicted_rssi;
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if(max_predicted_rssi < stats.rssi_min)
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stats.rssi_min = max_predicted_rssi;
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result.push(max_predicted_rssi);
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}
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return { result, stats };
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}
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}
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export default AIWrapper;
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