//Todo: Create dialog
private void optimizeParametersNN_btn_Click(object sender, EventArgs e)
{
new Thread(delegate() {
//Todo: Drop fields random
string[] inputFields = new string[] { "ssi-mt4", "spread", "mid-TradingTime", "mid-Stoch_600000", "mid-Stoch_1800000", "mid-Stoch_3600000", "mid-Stoch_7200000", "mid-Stoch_14400000", "mid-Stoch_21600000", "mid-MA_600000", "mid-MA_1800000", "mid-MA_3600000", "mid-MA_7200000", "mid-MA_14400000", "mid-MA_21600000", "mid-Range_1800000", "mid-Range_3600000", "mid-Range_7200000" };
string outputField = "buy-outcomeCode-0,001_600000";
int epochs = 10;
int networksCount = 500;
string path = Application.StartupPath + "/AI/";
GeneralExcelGenerator excel = new GeneralExcelGenerator(path + "excel.xlsx");
string sheetName = "OptimizeNN-E10";
string[] header = new string[2 + AdvancedNeuralNetwork.getExcelHeader().Length];
AdvancedNeuralNetwork.getExcelHeader().CopyTo(header, 2);
header[0] = "ValidationE";
header[1] = "TrainingE";
excel.CreateSheet(sheetName, header);
double[][] inputsTraining = new double[][] { };
double[][] outputsTraining = new double[][] { };
dataminingDb.getInputOutputArrays(inputFields, outputField, "EURUSD", ref inputsTraining, ref outputsTraining, DataGroup.All, 100 * 1000, 0);
double[][] inputsValidation = new double[][] { };
double[][] outputsValidation = new double[][] { };
dataminingDb.getInputOutputArrays(inputFields, outputField, "EURUSD", ref inputsValidation, ref outputsValidation, DataGroup.All, 10 * 1000, 1);
dataminingDb.unloadPair("EURUSD");
for (int ns = 0; ns < networksCount; ns++)
{
IMachineLearning network = AdvancedNeuralNetwork.getRandom(inputFields, outputField);
network.train(inputsTraining, outputsTraining, epochs);
double trainingError = network.getError();
double validationError = network.getPredictionErrorFromData(inputsValidation, outputsValidation);
string[] excelRow = new string[2 + network.getInfo(inputFields, outputField).Length];
network.getInfo(inputFields, outputField).CopyTo(excelRow, 2);
excelRow[0] = validationError.ToString();
excelRow[1] = trainingError.ToString();
excel.addRow(sheetName, excelRow);
setState("Optimizing NN " + ns + "/" + networksCount);
}
if (Directory.Exists(path) == false)
{
Directory.CreateDirectory(path);
}
excel.FinishSheet(sheetName);
excel.FinishDoc();
excel.save();
}).Start();
}
//Todo: Dialog, Training, Testing, Validation
private void create_ann_button_click(object sender, EventArgs e)
{
new Thread(delegate() {
string path = Application.StartupPath + "/AI/";
int maxEpochs = int.MaxValue;
string[] inputFields = new string[] { "ssi-mt4", "spread", "mid-TradingTime", "mid-Stoch_600000", "mid-Stoch_1800000", "mid-Stoch_3600000", "mid-Stoch_7200000", "mid-Stoch_14400000", "mid-Stoch_21600000", "mid-MA_600000", "mid-MA_1800000", "mid-MA_3600000", "mid-MA_7200000", "mid-MA_14400000", "mid-MA_21600000", "mid-Range_1800000", "mid-Range_3600000", "mid-Range_7200000" };
int[] neuronsCount = new int[] { inputFields.Length, 18, 12, 1 };
string outputField = "buy-outcomeCode-0,001_600000";
IMachineLearning network = new AdvancedNeuralNetwork(inputFields, outputField, neuronsCount, 0.1, 2, false, false, false, Accord.Neuro.Learning.JacobianMethod.ByBackpropagation);
double[][] inputs = new double[][] { };
double[][] outputs = new double[][] { };
dataminingDb.getInputOutputArrays(inputFields, outputField, "EURUSD", ref inputs, ref outputs, DataGroup.Training);
dataminingDb.unloadPair("EURUSD");
int epochsDone = 0;
double lastError = -1;
while (epochsDone < maxEpochs)
{
setState("e" + Math.Round(network.getError(), 4).ToString());
network.train(inputs, outputs, 1);
if (lastError == network.getError())
{
break;
}
else
{
lastError = network.getError();
}
if (Directory.Exists(path) == false)
{
Directory.CreateDirectory(path);
}
network.save(path + "E" + epochsDone + "_" + "e" + Math.Round(network.getError(), 4).ToString().Replace(',', '-').Replace('.', '-') + "_" + "EURUSD" + ".network"); //DateTime.Now.ToString("yyyy_dd_mm")
epochsDone++;
}
setState("Done learning");
}).Start();
}
