Scan the input data for invalid values

server/train-ai/AITrainer.mjs

@@ -100,12 +100,14 @@ class AITrainer {
 	async train_gateway(gateway_id, destination_filename) {
 		this.l.log(`${this.a.fgreen}${this.a.hicol}Training AI for gateway ${gateway_id}${this.a.reset}`);
 		
+		// Create the neural network
 		let net_settings = {
 			hiddenLayers: this.settings.ai.network_arch,
 			activation: "sigmoid"
 		};
 		let net = new brain.NeuralNetwork(net_settings);
 		
+		// Fetch the dataset
 		let dataset = this.dataset_fetcher.fetch_all(gateway_id);
 		
 		await net.trainAsync(dataset, {

server/train-ai/DatasetFetcher.mjs

@@ -21,11 +21,14 @@ class DatasetFetcher {
 	}
 	
 	fetch_all(gateway_id, extended = false) {
-		let gateway_location = this.repo_gateway.get_by_id(gateway_id);
-		
-		let iterator = gateway_id == null ? this.repo_rssi.iterate() : this.repo_rssi.iterate_gateway(gateway_id);
 		let result = [];
 		
+		// Determine the location of the gateway
+		let gateway_location = this.repo_gateway.get_by_id(gateway_id);
+		
+		// Grab an iterator for the data we want to add
+		let iterator = gateway_id == null ? this.repo_rssi.iterate() : this.repo_rssi.iterate_gateway(gateway_id);
+		// Add the readings where we did get a signal
 		for(let rssi of iterator) {
 			if(gateway_id == null)
 				gateway_location = this.repo_gateway.get_by_id(rssi.gateway_id);
@@ -49,6 +52,7 @@ class DatasetFetcher {
 			result.push(item);
 		}
 		
+		//  Add the readings where we did not get a signal
 		for(let reading of this.repo_reading.iterate_unreceived()) {
 			let item = {
 				input: {
@@ -66,8 +70,10 @@ class DatasetFetcher {
 			}
 			result.push(item);
 		}
-		shuffle_fisher_yates(result);
 		
+		// Zap the false negatives, but only if we're told to
+		// False neegatives are readings with not signal that are right next to 
+		// a reading with a signal, within a configurable radius.
 		if(this.settings.ai.do_zap_false_negatives) {
 			let zap_count_before = result.length,
 				zap_count = this.zap_false_negatives(
@@ -79,6 +85,7 @@ class DatasetFetcher {
 			this.l.log_e(`[DatasetFetcher] Zapped ${zap_count} false negatives with a radius of ${this.settings.ai.false_negative_zap_radius}m (${zap_count_before} -> ${zap_count_after} points).`);
 		}
 		
+		// Normalise all the values
 		for(let item of result) {
 			item.input.latitude = normalise_lat(item.input.latitude);
 			item.input.longitude = normalise_lng(item.input.longitude);
@@ -86,6 +93,12 @@ class DatasetFetcher {
 			item.output[0] = normalise_rssi(item.output[0]);
 		}
 		
+		// Shuffle the dataset
+		shuffle_fisher_yates(result);
+		
+		// Scan the resulting dataset for invalid items
+		this.scan_for_corruption(result);
+		
 		return result;
 	}
 	
@@ -129,6 +142,22 @@ class DatasetFetcher {
 		
 		return items_zapped;
 	}
+	
+	scan_for_corruption(dataset) {
+		// Scan the input data to make sure it is't corrupt
+		for(let row of dataset) {
+			if(isNaN(row.output[0]) || isNaN(row.input.latitude) || isNaN(row.input.longitude) || isNaN(row.input.distance)) {
+				console.error(row);
+				throw new Error("Error: Found NaN in input data");
+			}
+			if(typeof row.output[0] !== "number" || typeof row.input.latitude !== "number" || typeof row.input.longitude !== "number" || typeof row.input.distance !== "number") {
+				console.error(row);
+				throw new Error("Error: Found item with an invalid type in the input data");
+			}
+		}
+		
+		this.l.log_e(`Scanned ${dataset.length} rows of data for invalid values.`);
+	}
 }