Added new features

NeuralDetector/NeuralNetwork.cpp

@@ -4,6 +4,8 @@
 
 #include "NeuralNetwork.h"
 
+using namespace chrono;
+
 NeuralNetwork::NeuralNetwork()
 {
 	V0 = Mat();
@@ -40,11 +42,18 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
 	// outputError1: error on output for the current input and new calculated 
 	//               weighfactors, i.e. V1, W1
 	double outputError0, outputError1, sumSqrDiffError = MAX_OUTPUT_ERROR + 1;
+	double lowestError = 10;
+	Mat V0l, W0l;
 	Mat V1, W1;
 
-	cout << endl << "Starting neural training..." << endl;
+	cout << endl << "Starting neural training, this might take a while..." << endl;
 
 	int runs = 0;
+
+	milliseconds lasttime = timestamp();
+	milliseconds starttime = timestamp();
+	bool shown = false;
+
 	while ((sumSqrDiffError > MAX_OUTPUT_ERROR) && (runs < MAXRUNS)) {
 
 		sumSqrDiffError = 0;
@@ -67,16 +76,40 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
 
 			sumSqrDiffError += (outputError1 - outputError0) * (outputError1 - outputError0);
 
+			if (sumSqrDiffError < lowestError)
+			{
+				lowestError = sumSqrDiffError;
+				V0l = V0;
+				W0l = W0;
+			}
+
 			V0 = V1;
 			W0 = W1;
 		}
 		runs++;
 
-		if (runs % 1000 == 0)
-			cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")" << endl;
+		if (timestamp() > lasttime + milliseconds(15000) && !shown)
+		{
+			cout << "We are still working on it, hold on..." << endl;
+			shown = true;
+		}
+
+		if (runs % 500 == 0)
+		{
+			shown = false;
+			auto runtime = (timestamp() - lasttime) / 1000;
+			cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")(" << runtime.count() << "s)" << endl;
+
+			lasttime = timestamp();
+		}
 	}
 
-	cout << "Training complete in " << runs << " runs" << endl;
+	V0 = V0l;
+	W0 = W0l;
+
+	auto totalduration = (timestamp() - starttime) / 1000;
+	cout << "Training finished in " << runs << " runs, it took " << totalduration.count() << "s" << endl;
+	cout << "The lowest error rate was " << lowestError << ", we are savind the matching factors" << endl;
 
 	Mat inputVectorTrainingSet, outputVectorTrainingSet, outputVectorBPN;
 
@@ -116,9 +149,6 @@ void NeuralNetwork::Read()
 
 void NeuralNetwork::write()
 {
-	for (pair<int, string> p : classes)
-		cout << p.first << " - " << p.second << endl;
-
 	FileStorage fs("factors.yml", FileStorage::WRITE);
 	fs << "W0" << W0 << "V0" << V0;
 	fs.release();
@@ -218,5 +248,13 @@ void NeuralNetwork::mat_class(Mat& ref, string& name)
 		}
 	}
 
-	name = classes[index] + " " + to_string(maxvalue*100) + "%";
+	name = classes[index] + " - " + to_string(maxvalue*100) + "%";
+}
+
+milliseconds NeuralNetwork::timestamp()
+{
+	milliseconds ms = duration_cast<milliseconds>(
+		system_clock::now().time_since_epoch());
+
+	return ms;
 }