private void backgroundWorker1_DoWork(object sender, System.ComponentModel.DoWorkEventArgs e)
{
iterations = 0;
NetworkTeacher networkTeacher = new NetworkTeacher(_network, 0.00001);
List <Car> carsForTeaching = new List <Car>();
Cars = new DataNormalizer(Cars).Normalize();
foreach (var index in CarsChekedBox.CheckedIndices)
{
carsForTeaching.Add(Cars[(int)index]);
}
//MessageBox.Show("Выбрано " + carsForTeaching.Count + " авто для обучения.");
double err = networkTeacher.Teach(carsForTeaching);
double tmp = Convert.ToDouble(e.Argument);
while (err > tmp)
{
err = networkTeacher.Teach(carsForTeaching);
iterations++;
//backgroundWorker1.ReportProgress((int)err, "error");
}
}
static void Main(string[] args)
{
int trainStartCount = 13821;
int trainEndCount = 16420;
// Для блочного обучения указать:
int startDataSetIndex = 296848;
int endDataSetIndex = 306848;
#region Set process settings
Process thisProc = Process.GetCurrentProcess();
thisProc.PriorityClass = ProcessPriorityClass.High;
#endregion
const int receptors = 75;
const int numberOfOutputClasses = 1; // Количество наших классов
int[] neuronByLayer = { 50, 50, numberOfOutputClasses };
_fileManager = new FileManager();
var networkTeacher = new NetworkTeacher(neuronByLayer, receptors, 13, _fileManager)
{
Iteration = trainEndCount,
TestVectors = _fileManager.ReadVectors("inputDataTestPart_temp.txt")
};
//networkTeacher.PreparingLearningData(true);
if (networkTeacher.CheckMemory())
{
networkTeacher.TrainNet(startDataSetIndex, endDataSetIndex, trainStartCount);
networkTeacher.CommonTestColorized();
networkTeacher.Visualize();
networkTeacher.PrintLearnStatistic(startDataSetIndex, endDataSetIndex, true);
if (networkTeacher.CheckMemory())
{
networkTeacher.BackupMemory();
}
}
else
{
Console.WriteLine("Train failed!");
}
Console.ReadKey();
}
static void Main(string[] args)
{
int trainStartCount = 0;
int trainEndCount = 10000;
#region Set process settings
Process thisProc = Process.GetCurrentProcess();
thisProc.PriorityClass = ProcessPriorityClass.High;
#endregion
const int receptors = 75;
const int numberOfOutputClasses = 1; // Количество наших классов
int[] neuronByLayer = { 50, 50, numberOfOutputClasses };
_fileManager = new FileManager();
var networkTeacher = new NetworkTeacher(neuronByLayer, receptors, 46, _fileManager)
{
Iteration = trainEndCount,
TestVectors = _fileManager.ReadVectors("inputDataTestPart_temp.txt")
};
networkTeacher.PreparingLearningData(false);
if (networkTeacher.CheckMemory())
{
networkTeacher.TrainNet(trainStartCount);
networkTeacher.CommonTestColorized();
networkTeacher.PrintLearnStatistic(true);
if (networkTeacher.CheckMemory())
{
networkTeacher.BackupMemory();
}
}
else
{
Console.WriteLine("Train failed!");
}
Console.ReadKey();
}
