public double[][] StepTrainingMethod()
{
ANNParameterBUS.TrainingSize = 35;
PreprocessBUS preprocessBUS = new PreprocessBUS();
ANNTrainBUS annTrain = new ANNTrainBUS();
int len = ArrPattern.Length - ANNParameterBUS.TrainingSize;
double[][] dblActual_Forecast = new double[2][];
dblActual_Forecast[0] = new double[len];
dblActual_Forecast[1] = new double[len];
ArrPattern = Preprocess.DenormalizeByMinMax(ArrPattern);
DblActual_Forecast[0] = Preprocess.DenormalizeByMinMax(DblActual_Forecast[0]);
for (int i = 0; i < len; i++)
{
double[][] trainingSet = SelectTrainingSet(i);
preprocessBUS.FindMinMax(trainingSet);
trainingSet = preprocessBUS.PreprocessByMinMax(trainingSet);
annTrain.LoadDataSet(trainingSet);
annTrain.Main();
ANNModel.LoadModelFile();
ANNModel.OutInputLayer = preprocessBUS.PreprocessByMinMax(ArrPattern[i + ANNParameterBUS.TrainingSize - 1]);
dblActual_Forecast[1][i] = preprocessBUS.DenormalizeByMinMax(ANNModel.FeedforwardTraining());
dblActual_Forecast[0][i] = DblActual_Forecast[0][i + ANNParameterBUS.TrainingSize];
}
DblActual_Forecast = dblActual_Forecast;
NumPattern = len;
return dblActual_Forecast;
}
private void btnTrain_Click(object sender, EventArgs e)
{
if (tbxTrainFilePath.Text == "")
{
MessageBox.Show("Error: You must fill all required inputs!", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
int iMeasureType = 0;
switch (cmbTrainingMeasure.SelectedItem.ToString())
{
case "MSE":
iMeasureType = MSE;
break;
case "NMSE":
iMeasureType = NMSE;
break;
case "RMSE":
iMeasureType = RMSE;
break;
case "Sign":
iMeasureType = SIGN;
break;
}
int iCrossValidationType;
if(rdShuffle.Checked)
{
iCrossValidationType = 0;
}
else if(rdNotShuffle.Checked)
{
iCrossValidationType = 1;
}
else
{
iCrossValidationType = 2;
}
#region Dự đoán giá
if (rdPricePrediction.Checked)//Dự đoán giá
{
int iPos = tbxTrainFilePath.Text.LastIndexOf('_');
string strMutualPath = tbxTrainFilePath.Text.Remove(iPos + 1);
string strModelFile = strMutualPath + "model.txt";
#region SVR
if (rdSVR.Checked)//Mô hình SVR
{
string[] strArgs = new string[8];
strArgs[0] = "-g"; //gama. Trong hàm Gauss kernel: exp(-gamma*|u-v|^2)
strArgs[2] = "-c";
strArgs[4] = "-p"; //epsilon
strArgs[6] = tbxTrainFilePath.Text;
strArgs[7] = strModelFile;
if (cmbModelSelection.SelectedItem.ToString() == "Use default values")
{
strArgs[1] = "0"; //gama
strArgs[3] = "1"; //c
strArgs[5] = "0.1"; //epsilon
//strArgs[1] = "0.686"; //gama
//strArgs[3] = "68.4717"; //c
//strArgs[5] = "0.00012124"; //epsilon
//strArgs[1] = "2.5"; //gama
//strArgs[3] = "0.01"; //c
//strArgs[5] = "0.3"; //epsilon
svm_train.Main(strArgs);
}
else if (cmbModelSelection.SelectedItem.ToString() == "Pattern search")
{
string[] strArgsModel = new string[4];
strArgsModel[0] = "-v";
strArgsModel[1] = tbxNumFold.Text;
strArgsModel[2] = tbxTrainFilePath.Text;
strArgsModel[3] = strModelFile;
int iNumModel = 10;
StreamWriter writer = new StreamWriter(strModelFile);
writer.WriteLine("Multiple");
writer.WriteLine(iNumModel.ToString());
StreamWriter patternTime = new StreamWriter(strMutualPath + "PatternTime.txt");
double dblTotalTime = 0;
for (int i = 0; i < iNumModel; i++)
{
svm_modelSelection modelSelection = new svm_modelSelection();
DateTime start = DateTime.Now;
modelSelection.Run(strArgsModel, strMutualPath + "PatternSearchLog" + (i + 1).ToString() + ".txt", false, iMeasureType, iCrossValidationType);
DateTime end = DateTime.Now;
TimeSpan elapsedTime = end - start;
dblTotalTime += elapsedTime.TotalMinutes;
patternTime.WriteLine((i + 1).ToString() + ": " + elapsedTime.TotalMinutes.ToString() + " minutes");
strArgs[1] = (Math.Pow(2, modelSelection.Y)).ToString(); //gama
strArgs[3] = (Math.Pow(2, modelSelection.X)).ToString(); //c
strArgs[5] = (Math.Pow(2, modelSelection.Z)).ToString(); //epsilon
//strArgs[3] = (Math.Pow(2, 6.09743805699862)).ToString();
//strArgs[1] = (Math.Pow(2, -3.86541887296616)).ToString();
//strArgs[5] = (Math.Pow(2, -13.0097538228192)).ToString();
strArgs[7] = strMutualPath + "model" + (i + 1).ToString() + ".txt";
writer.WriteLine(strArgs[7]);
svm_train.Main(strArgs);
}
writer.Close();
patternTime.WriteLine("Total: " + dblTotalTime.ToString() + " minutes");
patternTime.Close();
}
else if (cmbModelSelection.SelectedItem.ToString() == "Grid search")
{
svm_modelSelection modelSelection = new svm_modelSelection();
string[] strArgsModel = new string[4];
strArgsModel[0] = "-v";
strArgsModel[1] = tbxNumFold.Text;
strArgsModel[2] = tbxTrainFilePath.Text;
strArgsModel[3] = strModelFile;
DateTime start = DateTime.Now;
modelSelection.Run(strArgsModel, strMutualPath + "GridSearchLog.txt", true, iMeasureType, iCrossValidationType);
DateTime end = DateTime.Now;
TimeSpan elapsedTime = end - start;
StreamWriter gridTime = new StreamWriter(strMutualPath + "GridTime.txt");
gridTime.WriteLine(elapsedTime.TotalMinutes.ToString() + " minutes");
gridTime.Close();
strArgs[1] = modelSelection.Y.ToString(); //gama
strArgs[3] = modelSelection.X.ToString(); //c
strArgs[5] = modelSelection.Z.ToString(); //epsilon
svm_train.Main(strArgs);
}
MessageBox.Show("Finish!");
}
#endregion
#region ANN
else//Mô hình ANN
{
ANNParameterBUS.InputNode = int.Parse(tbxNumInputNode.Text);
ANNParameterBUS.HiddenNode = int.Parse(tbxANNHiddenNode.Text);
ANNParameterBUS.OutputNode = 1;
ANNParameterBUS.MaxEpoch = int.Parse(tbxMaxLoops.Text);
ANNParameterBUS.LearningRate = double.Parse(tbxLearningRate.Text);
ANNParameterBUS.Momentum = double.Parse(tbxMomentum.Text);
ANNParameterBUS.Bias = double.Parse(tbxBias.Text);
ANNModelBUS.AnnModelFile = strModelFile;
ANNTrainBUS annTrain = new ANNTrainBUS();
annTrain.LoadDataSet(tbxTrainFilePath.Text);
annTrain.Main();
MessageBox.Show("Finish!");
}
#endregion
}
#endregion
#region Dự đoán xu hướng
else//Dự đoán xu hướng
{
if (rdSVR.Checked)//Mô hình SVR
{
int iPos = tbxTrainFilePath.Text.LastIndexOf('_');
string strMutualPath = tbxTrainFilePath.Text.Remove(iPos + 1);
string strModelFile = strMutualPath + "model.txt";
string[] strArgs = new string[8];
strArgs[0] = "-g"; //gama. Trong hàm Gauss kernel: exp(-gamma*|u-v|^2)
strArgs[2] = "-c";
strArgs[4] = "-p"; //epsilon
strArgs[6] = tbxTrainFilePath.Text;
strArgs[7] = strModelFile;
if (cmbModelSelection.SelectedItem.ToString() == "Use default values")
{
//strArgs[1] = "0"; //gama
//strArgs[3] = "1"; //c
//strArgs[5] = "0.1"; //epsilon
strArgs[1] = "0.0110485434560398"; //gama
strArgs[3] = "304.437021440697"; //c
strArgs[5] = "0.000290333768311211"; //epsilon
//strArgs[1] = "2.5"; //gama
//strArgs[3] = "0.01"; //c
//strArgs[5] = "0.3"; //epsilon
svm_train.Main(strArgs);
}
else if (cmbModelSelection.SelectedItem.ToString() == "Pattern search")
{
string[] strArgsModel = new string[4];
strArgsModel[0] = "-v";
strArgsModel[1] = tbxNumFold.Text;
strArgsModel[2] = tbxTrainFilePath.Text;
strArgsModel[3] = strModelFile;
int iNumModel = 10;
StreamWriter writer = new StreamWriter(strModelFile);
writer.WriteLine("Multiple");
writer.WriteLine(iNumModel.ToString());
for (int i = 0; i < iNumModel; i++)
{
svm_modelSelection modelSelection = new svm_modelSelection();
modelSelection.Run(strArgsModel, strMutualPath + "PatternSearchLog" + (i + 1).ToString() + ".txt", false, iMeasureType, iCrossValidationType);
strArgs[1] = (Math.Pow(2, modelSelection.Y)).ToString(); //gama
strArgs[3] = (Math.Pow(2, modelSelection.X)).ToString(); //c
strArgs[5] = (Math.Pow(2, modelSelection.Z)).ToString(); //epsilon
//strArgs[3] = (Math.Pow(2, 6.09743805699862)).ToString();
//strArgs[1] = (Math.Pow(2, -3.86541887296616)).ToString();
//strArgs[5] = (Math.Pow(2, -13.0097538228192)).ToString();
strArgs[7] = strMutualPath + "model" + (i + 1).ToString() + ".txt";
writer.WriteLine(strArgs[7]);
svm_train.Main(strArgs);
}
writer.Close();
}
else if (cmbModelSelection.SelectedItem.ToString() == "Grid search")
{
svm_modelSelection modelSelection = new svm_modelSelection();
string[] strArgsModel = new string[4];
strArgsModel[0] = "-v";
strArgsModel[1] = tbxNumFold.Text;
strArgsModel[2] = tbxTrainFilePath.Text;
strArgsModel[3] = strModelFile;
modelSelection.Run(strArgsModel, strMutualPath + "GridSearchLog.txt", true, iMeasureType, iCrossValidationType);
strArgs[1] = modelSelection.Y.ToString(); //gama
strArgs[3] = modelSelection.X.ToString(); //c
strArgs[5] = modelSelection.Z.ToString(); //epsilon
svm_train.Main(strArgs);
}
MessageBox.Show("Finish!");
}
else//Mô hình ANN
{
int iPos = tbxTrainFilePath.Text.LastIndexOf('_');
string strModelFile = tbxTrainFilePath.Text.Remove(iPos + 1) + "model.txt";
//khởi tạo các tham số cho mạng
ANNParameterBUS.InputNode = int.Parse(tbxNumInputNode.Text);
ANNParameterBUS.HiddenNode = int.Parse(tbxANNHiddenNode.Text);
ANNParameterBUS.OutputNode = 1;
ANNParameterBUS.MaxEpoch = int.Parse(tbxMaxLoops.Text);
ANNParameterBUS.LearningRate = double.Parse(tbxLearningRate.Text);
ANNParameterBUS.Momentum = double.Parse(tbxMomentum.Text);
ANNParameterBUS.Bias = double.Parse(tbxBias.Text);
ANNParameterBUS.Accuracy = double.Parse(tbxAccuracy.Text);
ANNParameterBUS.MeasureType = cmbTrainingMeasure.SelectedItem.ToString();
//Tiến hành train
ANNModelBUS.AnnModelFile = strModelFile;
ANNTrainBUS annTrain = new ANNTrainBUS();
annTrain.LoadDataSet(tbxTrainFilePath.Text);
annTrain.Main(iMeasureType);
MessageBox.Show("Finish!");
}
#endregion
}
}
//Giá trị trả về là giá dự đoán
public double TrainAndPredict(bool isSVR)
{
double[][] dblActual_Forecast = null;
#region Phần chung
//1. Lấy đoạn dữ liệu cần thiết
double[] dblData = PickNeededData();
//2. Tiền xử lý
PreprocessBUS preprocessBUS = new PreprocessBUS();
if (Preprocess == "ScaleByMinMax")
{
preprocessBUS.FindMinMax(dblData);
dblData = preprocessBUS.PreprocessByMinMax(dblData);
}
ConverterBUS converter = new ConverterBUS();
int iNumLine = 0;
if(ImprovedDirection)
{
converter.ConvertWImprovedDirection(NumInputNode, dblData, "Training.txt", 100, out iNumLine);
}
else
{
converter.Convert(NumInputNode, dblData, "Training.txt", out iNumLine);
}
//Tạo file test. File test này chỉ có 2 dòng:
//+Dòng 1: Chứa thông tin tiền xử lý
//+Dòng 2: Giống như 1 dòng của file training, nhưng giá trị đích không biết (để bằng 0)
StreamWriter writer = new StreamWriter("Test.txt");
if (Preprocess == "ScaleByMinMax")
{
writer.WriteLine("ScaleByMinMax " + preprocessBUS.Min.ToString() + " " + preprocessBUS.Max.ToString());
}
string strLine = "0 ";
for (int i = 0; i < NumInputNode; i++)
{
strLine = strLine + (i+1).ToString() + ":" + dblData[TrainingSize - NumInputNode + i].ToString() + " ";
}
writer.WriteLine(strLine);
writer.Close();
#endregion
#region Phần riêng
//3. Huấn luyện và dự đoán
#region SVR
if (isSVR == true) //SVR
{
string[] strArgs = new string[8];
strArgs[0] = "-g"; //gama. Trong hàm Gauss kernel: exp(-gamma*|u-v|^2)
strArgs[2] = "-c";
strArgs[4] = "-p"; //epsilon
strArgs[6] = "Training.txt";
strArgs[7] = "SVRModel.txt";
//Tìm bộ tham số (gama, c, epsilon)
svm_modelSelection modelSelection = new svm_modelSelection();
modelSelection.Log = false;
string[] strArgsModel = new string[4];
strArgsModel[0] = "-v";
strArgsModel[1] = NumFold;
strArgsModel[2] = "Training.txt";
strArgsModel[3] = "SVRModel.txt";
if (ModelSelection == "Grid search")
{
modelSelection.Run(strArgsModel, "", true, TrainingMeasure, CrossValidationType);
strArgs[1] = modelSelection.Y.ToString(); //gama
strArgs[3] = modelSelection.X.ToString(); //c
strArgs[5] = modelSelection.Z.ToString(); //epsilon
//Huấn luyện
svm_train.Main(strArgs);
//Dự đoán
strArgs = new string[3];
strArgs[0] = "Test.txt";
strArgs[1] = "SVRModel.txt";
strArgs[2] = "Prediction.txt";
dblActual_Forecast = svm_predict.MainProcess(strArgs);
}
else if(ModelSelection == "Pattern search")
{
const int iNumModel = 10;
for (int i = 0; i < iNumModel; i++)
{
modelSelection = new svm_modelSelection {Log = false};
strArgsModel[3] = "SVRModel" + i + 1 + ".txt";
modelSelection.Run(strArgsModel, "", false, TrainingMeasure, CrossValidationType);
strArgs[1] = (Math.Pow(2, modelSelection.Y)).ToString(); //gama
strArgs[3] = (Math.Pow(2, modelSelection.X)).ToString(); //c
strArgs[5] = (Math.Pow(2, modelSelection.Z)).ToString(); //epsilon
strArgs[7] = "SVRModel" + i + 1 + ".txt";
//Huấn luyện
svm_train.Main(strArgs);
}
//Dự đoán
double[][][] dblTemp = new double[iNumModel][][];
dblActual_Forecast = new double[2][];
dblActual_Forecast[0] = new double[]{0};
dblActual_Forecast[1] = new double[]{0};
for (int i = 0; i < iNumModel; i++)
{
strArgs = new string[3];
strArgs[0] = "Test.txt";
strArgs[1] = "SVRModel" + i + 1 + ".txt";
strArgs[2] = "Prediction.txt";
dblTemp[i] = svm_predict.MainProcess(strArgs);
dblActual_Forecast[1][0] += dblTemp[i][1][0];
}
dblActual_Forecast[1][0] /= iNumModel;
}
}
#endregion
#region ANN
else //ANN
{
//Huấn luyện
ANNParameterBUS.InputNode = NumInputNode;
ANNModelBUS.AnnModelFile = "ANNModel.txt";
ANNTrainBUS annTrain = new ANNTrainBUS();
annTrain.LoadDataSet("Training.txt");
annTrain.Main();
//Dự đoán
ANNParameterBUS.LoadParameter();
ANNPredictBUS annPredict = new ANNPredictBUS();
annPredict.LoadDataSet("Test.txt");
dblActual_Forecast = annPredict.MainProcess();
}
#endregion
#endregion
return dblActual_Forecast[1][0]; //Giá dự đoán
}