// please before call this fuction set 2 properties and then call generate method
public MyCategorizedTimeSeri <T> generate(int numberOfTests, int numberOfForecastTests)
{
MyTimeSeri <T> tmp = generate();
MyCategorizedTimeSeri <T> myCategorizedTimeSeri = new MyCategorizedTimeSeri <T>();
int rows = tmp.inputs.GetLength(0) - numberOfTests - numberOfForecastTests;
myCategorizedTimeSeri.TimeSeri = TimeSeri;
myCategorizedTimeSeri.TrainInputs = new T[rows, NumberOfInputVariables];
myCategorizedTimeSeri.TrainTargets = new T[rows];
myCategorizedTimeSeri.TestInputs = new T[numberOfTests, NumberOfInputVariables];
myCategorizedTimeSeri.TestTargets = new T[numberOfTests];
myCategorizedTimeSeri.ForecastTestInputs = new T[numberOfForecastTests, NumberOfInputVariables];
myCategorizedTimeSeri.ForecastTestTargets = new T[numberOfForecastTests];
int l = 0;
for (; l < rows; l++)
{
for (int j = 0; j < NumberOfInputVariables; j++)
{
myCategorizedTimeSeri.TrainInputs[l, j] = tmp.inputs[l, j];
}
myCategorizedTimeSeri.TrainTargets[l] = tmp.targets[l];
}
for (int i = 0; i < numberOfTests; i++, l++)
{
for (int j = 0; j < NumberOfInputVariables; j++)
{
myCategorizedTimeSeri.TestInputs[i, j] = tmp.inputs[l, j];
}
myCategorizedTimeSeri.TestTargets[i] = tmp.targets[l];
}
for (int i = 0; i < numberOfForecastTests; i++, l++)
{
for (int j = 0; j < NumberOfInputVariables; j++)
{
myCategorizedTimeSeri.ForecastTestInputs[i, j] = tmp.inputs[l, j];
}
myCategorizedTimeSeri.ForecastTestTargets[i] = tmp.targets[l];
}
return(myCategorizedTimeSeri);
}
public void Start()
{
try
{
svmWriter = new StreamWriter("Complex_Hybrid_SVMOutput.txt");
arimaLogger = new StreamWriter("Complex_Hybrid_ArimaGALog.txt");
hybridWriter = new StreamWriter(OUTPUT_FILE_NAME);
#region Loading the training data and classes and test data and test classes
TimeSeriGenerator <float> timeSeriGenerator = new TimeSeriGenerator <float>();
int numInp = 0;
this.Dispatcher.Invoke(new Action(() => numInp = Int32.Parse(NumberOfInpTextBox.Text)));
timeSeriGenerator.load(numInp);
Dispatcher.Invoke(new Action(() => ActivityProgressBar.IsIndeterminate = true));
Dispatcher.Invoke(new Action(() => numberOfTests = Int32.Parse(OptimumTestTextBox.Text)));
Dispatcher.Invoke(new Action(() => numberOfForecastTests = Int32.Parse(ForecastTestTextBox.Text)));
myCategorizedTimeSeri = timeSeriGenerator.generate(numberOfTests, numberOfForecastTests);
#endregion
#region creating and training the svm model
double minError = 9999999;
SVM_KERNEL_TYPE bestKernelType = SVM_KERNEL_TYPE.LINEAR;
double bestEps = 0.1;
SVMParams p;
Matrix <float> trainData = new Matrix <float>(myCategorizedTimeSeri.TrainInputs);
Matrix <float> trainClasses = new Matrix <float>(myCategorizedTimeSeri.TrainTargets);
Matrix <float> testData = new Matrix <float>(myCategorizedTimeSeri.TestInputs);
Matrix <float> testClasses = new Matrix <float>(myCategorizedTimeSeri.TestTargets);
foreach (SVM_KERNEL_TYPE tp in Enum.GetValues(typeof(SVM_KERNEL_TYPE)))
{
for (double eps = 0.1; eps >= 0.00001; eps *= 0.1)
{
using (SVM model = new SVM())
{
p = new SVMParams();
p.KernelType = tp;
p.SVMType = SVM_TYPE.EPS_SVR; // for regression
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, eps);
p.Gamma = 1;
p.Degree = 1;
p.P = 1;
p.Nu = 0.1;
bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 10);
double error = getSumError(model, testData, testClasses);
if (trained && minError > error)
{
minError = error;
bestEps = eps;
bestKernelType = tp;
}
}
}
}
Matrix <float> trainDataWithGATest = new Matrix <float>(myCategorizedTimeSeri.getTrainWithTestInputs());
Matrix <float> trainClassesWithGATest = new Matrix <float>(myCategorizedTimeSeri.getTrainWithTestTargets());
svmModel = new SVM();
p = new SVMParams();
p.KernelType = bestKernelType;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.EPS_SVR; // for regression
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, bestEps);
p.Gamma = 1;
p.Degree = 1;
p.P = 1;
p.Nu = 0.1;
bool _trained = svmModel.TrainAuto(trainDataWithGATest, trainClassesWithGATest, null, null,
p.MCvSVMParams, 10);
List <float> Et = getResidual(trainDataWithGATest, trainClassesWithGATest);
svmWriter.Flush();
svmWriter.Close();
int bestD = StartArima(Et.ToArray());
List <float> Zt = new List <float>();
float mu = Et.Average();
if (bestD == 0)
{
for (int i = 0; i < Et.Count; i++)
{
Zt.Add(Et[i] - mu);
}
}
else if (bestD == 1)
{
Zt.Add(0);
for (int i = 1; i < Et.Count; i++)
{
Zt.Add(Et[i] - Et[i - 1] - mu);
}
}
else //else if (bestD == 2) << CHECK HERE >>
{
Zt.Add(0);
Zt.Add(0);
for (int i = 2; i < Et.Count; i++)
{
Zt.Add(Et[i] - 2 * Et[i - 1] + Et[i - 2] - mu);
}
}
Pair <int> bestAB = CreateComplexHybridModel(Et.ToArray(), Zt.ToArray());
MessageBox.Show(bestAB.First + " , " + bestAB.Second, "INJAAAAAAAAAAAAAAAAA", MessageBoxButton.OK,
MessageBoxImage.Asterisk);
// now our complex hybrid model is created
double minErr = SVMComplexModelForBestModel(bestAB.First, bestAB.Second, Et.ToArray(), Zt.ToArray());
MessageBox.Show("MinError In Training => " + minErr);
double mse = 0;
double errorPercent = 0;
double sumTargets = 0;
List <float> results = new List <float>();
Matrix <float> testIn = new Matrix <float>(myCategorizedTimeSeri.ForecastTestInputs);
Queue <float> EtQueue = new Queue <float>();
Queue <float> ZtQueue = new Queue <float>();
for (int i = 0; i < bestAB.First; i++)
{
EtQueue.Enqueue(Et[Et.Count - bestAB.First + i]);
}
for (int i = 0; i < bestAB.Second; i++)
{
ZtQueue.Enqueue(Zt[Zt.Count - bestAB.Second + i]);
}
for (int i = 0; i < numberOfForecastTests; i++)
{
float Lt = svmModel.Predict(testIn.GetRow(i));
float[] inpTest = new float[bestAB.First + bestAB.Second + 1];
float[] EQArray = EtQueue.ToArray();
float[] ZQArray = ZtQueue.ToArray();
int l = 0;
for (int j = 0; j < bestAB.First; j++, l++)
{
inpTest[l] = EQArray[j];
}
inpTest[l++] = Lt;
for (int j = 0; j < bestAB.Second; j++, l++)
{
inpTest[l] = ZQArray[j];
}
float result = svmModelHybrid.Predict(new Matrix <float>(inpTest));
results.Add(result);
hybridWriter.WriteLine(result);
float target = myCategorizedTimeSeri.TestTargets[i];
//mse += Math.Pow(target - result, 2);
//errorPercent += Math.Abs(target - result);
//sumTargets += Math.Abs(target);
// preparing for next use in this for loop
float resi = target - Lt; // float resi = target - result; << CHECK HERE IMPORTANT >>
Et.Add(resi);
EtQueue.Dequeue();
EtQueue.Enqueue(resi);
ZtQueue.Dequeue();
mu = Et.Average();
if (bestD == 0)
{
ZtQueue.Enqueue(EQArray[EQArray.Length - 1] - mu);
}
else if (bestD == 1)
{
ZtQueue.Enqueue(EQArray[EQArray.Length - 1] - EQArray[EQArray.Length - 2] - mu);
}
else //else if (bestD == 2) << CHECK HERE >>
{
ZtQueue.Enqueue(EQArray[EQArray.Length - 1] - 2 * EQArray[EQArray.Length - 2] +
EQArray[EQArray.Length - 3] - mu);
}
}
//mse /= numberOfForecastTests;
//hybridWriter.WriteLine("\n\nMSE => " + mse);
//errorPercent /= sumTargets;
//hybridWriter.WriteLine("\n\nERROR% => " + errorPercent*100);
double _mse = MyErrorParameters.MSE(results.ToArray(), myCategorizedTimeSeri.ForecastTestTargets);
double _errorPercent = MyErrorParameters.ERROR_Percent(results.ToArray(), myCategorizedTimeSeri.ForecastTestTargets);
hybridWriter.WriteLine("\n\n\nMSE & ERROR% are =>\n\n{0} {1}", _mse, _errorPercent);
hybridWriter.Flush();
hybridWriter.Close();
MessageBox.Show(
String.Format(
"Complex Hybrid Model Created File {0} For Output Successfully Now , Please Check It Out .",
OUTPUT_FILE_NAME), "Hybrid SVM Arima Done", MessageBoxButton.OK,
MessageBoxImage.Information);
#endregion
}
catch (Exception ex)
{
MessageBox.Show(ex.Message + "\n\n" + ex.StackTrace, "ERROR", MessageBoxButton.OK, MessageBoxImage.Error);
}
}
