public static void Learn(INeuralDataSet learningSet, INeuralDataSet testingSet, int inputSize, InputClass inputData, int testingSize, int index)
{
string logDir = System.IO.Path.GetDirectoryName(Application.ExecutablePath);
int iteracje = inputData.iterations;
List <double> errors = new List <double>();
Console.WriteLine("Tworze siec...");
ITrain Network = NetworkHelper.CreateNeuronNetwork(learningSet, inputSize, inputData);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int iteracja = 0;
do
{
Network.Iteration();
errors.Add(Network.Error);
iteracja++;
} while ((iteracja < iteracje) && (Network.Error > 0.0001) && (Network.Error < 10000));
stopwatch.Stop();
String timeStamp = NetworkHelper.GetTimestamp(DateTime.Now);
/// TUTAJ SIEC SIE TEORETYCZNIE NAUCZYLA
/// TERAZ ZBIOR TESTOWY, WYNIKI
/// I WYKRES ERRORA
///
string errDir = logDir + "\\ERRORS\\err_" + timeStamp + "_" + ".r";
string log_name = timeStamp + "_" + index;
Logger(inputData.ToString(), logDir, log_name);
double[] neuralAnswer = new double[testingSize];
int i = 0;
foreach (INeuralDataPair pair in testingSet)
{
INeuralData output = Network.Network.Compute(pair.Input);
double small = 0.0;
for (int r = 0; r < 4; r++)
{
if ((double)(output[r]) >= small)
{
neuralAnswer[i] = (double)r;
small = (double)(output[r]);
}
}
i++;
}
int[] answers = NetworkHelper.DenormaliseAnswers(neuralAnswer);
Logger("Neural Network Learning Result" + Environment.NewLine, logDir, log_name);
Logger(errors[errors.Count - 1] + " %" + Environment.NewLine, logDir, log_name);
Logger("Neural Network Testing Result" + Environment.NewLine, logDir, log_name);
double calculateError = NetworkHelper.CalculateFinalError(answers, testingSet);
Logger("Error: " + calculateError + " %" + Environment.NewLine, logDir, log_name);
Logger(String.Format("Time elapsed: {0:hh\\:mm\\:ss}", stopwatch.Elapsed) + Environment.NewLine, logDir, log_name);
Console.WriteLine("FINISH");
NetworkHelper.CreateErrorFile(errors, errDir);
}
public static ITrain LearnNetwork(INeuralDataSet learningSet, INeuralDataSet testingSet, int inputSize, InputClass inputData, int testingSize)
{
string logDir = System.IO.Path.GetDirectoryName(Application.ExecutablePath);
int iteracje = inputData.iterations;
List <double> errors = new List <double>();
Console.WriteLine("Tworze siec...");
ITrain Network = CreateNeuronNetwork(learningSet, inputSize, inputData);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int iteracja = 0;
do
{
Network.Iteration();
Console.WriteLine("Epoch #" + iteracja + " Error:" + Network.Error);
errors.Add(Network.Error);
iteracja++;
} while ((iteracja < iteracje) && (Network.Error > 0.0001) && (Network.Error < 10000));
stopwatch.Stop();
String timeStamp = GetTimestamp(DateTime.Now);
/// TUTAJ SIEC SIE TEORETYCZNIE NAUCZYLA
/// TERAZ ZBIOR TESTOWY, WYNIKI
/// I WYKRES ERRORA
///
string errDir = logDir + "ERRORS\\err_" + timeStamp + ".r";
Logger(inputData.ToString(), logDir, timeStamp);
double[] neuralAnswer = new double[testingSize];
int i = 0;
foreach (INeuralDataPair pair in testingSet)
{
INeuralData output = Network.Network.Compute(pair.Input);
double small = 0.0;
for (int r = 0; r < 4; r++)
{
if ((double)(output[r]) >= small)
{
neuralAnswer[i] = (double)r;
small = (double)(output[r]);
}
}
i++;
}
int[] answers = DenormaliseAnswers(neuralAnswer);
Logger("Neural Network Learning Result" + Environment.NewLine, logDir, timeStamp);
Logger(errors[errors.Count - 1] + " %" + Environment.NewLine, logDir, timeStamp);
Console.WriteLine("Neural Network Results");
Logger("Neural Network Testing Result" + Environment.NewLine, logDir, timeStamp);
double calculateError = CalculateFinalError(answers, testingSet);
Console.WriteLine("Error: " + calculateError + " %");
Logger("Error: " + calculateError + " %" + Environment.NewLine, logDir, timeStamp);
Console.WriteLine("Time elapsed: {0:hh\\:mm\\:ss}", stopwatch.Elapsed);
Logger(String.Format("Time elapsed: {0:hh\\:mm\\:ss}", stopwatch.Elapsed) + Environment.NewLine, logDir, timeStamp);
Console.WriteLine("FINISH");
if ((errors[errors.Count - 1] * 100).ToString().Length > 4)
{
inputData.learningError = (errors[errors.Count - 1] * 100).ToString().Substring(0, 4) + " %";
}
else
{
inputData.learningError = (errors[errors.Count - 1] * 100).ToString() + " %";
}
if (calculateError.ToString().Length > 4)
{
inputData.testingError = calculateError.ToString().Substring(0, 4) + " %";
}
else
{
inputData.testingError = calculateError.ToString() + " %";
}
inputData.timeElapsed = stopwatch.Elapsed.Hours + "h " + stopwatch.Elapsed.Minutes + "min " + stopwatch.Elapsed.Seconds + "sec";
CreateErrorFile(errors, errDir);
return(Network);
}
