85 lines
1.8 KiB
JavaScript
85 lines
1.8 KiB
JavaScript
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"use strict";
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import path from 'path';
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import {
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loadLayersModel as tf_loadLayersModel,
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tensor as tf_tensor
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} from '@tensorflow/tfjs';
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import { normalise } from '../../common/Math.mjs';
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import Config from '../ClientConfig.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.gateways = new Map();
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}
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async setup({ bounds, index }) {
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this.map_bounds = bounds;
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this.index = index;
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for(let gateway of this.index.index) {
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this.gateways.set(
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gateway.id,
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await tf_loadLayersModel(`${window.location.href}/${path.dirname(Config.ai_index_file)}/${gateway.id}/model.json`)
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);
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}
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this.setup_complete = true;
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}
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predict_row(lat) {
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if(!setup_complete)
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throw new Error("Error: Can't do predictions until the setup is complete.");
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let results = [],
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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 += Config.step.lng) {
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let max_predicted_rssi = -Infinity;
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for(let [, ai] of this.gateways) {
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let next_prediction = this.predict_value(lat, lng)
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max_predicted_rssi = Math.max(
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max_predicted_rssi,
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next_prediction
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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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predict_value(latitude, longitude) {
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return ai.predict(
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tf_tensor([ latitude, longitude ], [1, 2])
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).arraySync()[0][0];
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}
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}
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export default AIWrapper;
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