125 lines
2.9 KiB
JavaScript
125 lines
2.9 KiB
JavaScript
"use strict";
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import brain from 'brain.js';
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import haversine from 'haversine-distance';
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import {
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normalise_lat, normalise_lng,
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normalise_gateway_distance,
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unnormalise_rssi
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} from '../../../common/Normalisers.mjs';
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class AIWrapper {
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get training_mode() {
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return this.index.properties.training_mode;
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}
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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(/*gateway.net_settings*/);
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net.fromJSON(
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gateway.frozen_net
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);
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this.gateways.set(
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gateway.id,
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{ ai: net, latitude: gateway.latitude, longitude: gateway.longitude }
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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*/, gateway] of this.gateways) {
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// Generate the input data
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let distance_from_gateway = haversine(
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{ latitude: lat, longitude: lng },
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gateway
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);
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let input_data = {
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latitude: normalise_lat(lat),
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longitude: normalise_lng(lng)
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};
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if(this.training_mode !== "unified")
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input_data.distance = normalise_gateway_distance(distance_from_gateway);
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// Validate the input data
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if(Number.isNaN(input_data.latitude)
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|| Number.isNaN(input_data.longitude)
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|| (
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this.training_mode !== "unified"
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&& Number.isNaN(input_data.distance))
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) {
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console.error(input_data);
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throw new Error("Error: Invalid neural network input.");
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}
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// Run the input through the neural network
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let next_value = gateway.ai.run(input_data);
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if(Number.isNaN(next_value[0])) {
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console.log(next_value);
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throw new Error("Error: Neural network returned NaN");
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}
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// Operate on the output
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max_predicted_rssi = Math.max(
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max_predicted_rssi,
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next_value[0]
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);
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}
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// Un-normalise the output of the neural nentwork to something sensible
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max_predicted_rssi = unnormalise_rssi(max_predicted_rssi);
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// Record the statistics
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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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