private void button1_Click(object sender, RoutedEventArgs e)
{
try
{
TimeSeriGenerator <float> timeSeriGenerator = new TimeSeriGenerator <float>();
if (timeSeriGenerator.load(Int32.Parse(NumberOfInpTextBox.Text)))
{
timeSeriGenerator.generate().write2FileWithBrowse();
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message + "\n" + ex.StackTrace, "ERROR", MessageBoxButton.OK, MessageBoxImage.Error);
}
}
private double[] LoadData()
{
double[] data = null;
try
{
int number = Int32.Parse(ColumnNumberTextBox.Text);
var timeSeriGenerator = new TimeSeriGenerator <double>();
timeSeriGenerator.load(number);
data = timeSeriGenerator.TimeSeri;
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "ERROR", MessageBoxButton.OK, MessageBoxImage.Error);
}
return(data);
}
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);
}
}
void Start()
{
var Et = new List <double>();
var Zt = new List <double>();
var Lt = new List <double>();
timeSeriGenerator = new TimeSeriGenerator <double>();
arimaLogger = new StreamWriter("Best_Hybrid_ArimaGALog.txt");
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)));
MyTimeSeriForBestHybrid <double> myTimeSeriForBestHybrid =
timeSeriGenerator.generateForBestHybrid(numberOfForecastTests);
//maxGAIteretionInArima = 1000;
//var train = new double[timeSeriGenerator.TimeSeri.Length - 5];
//var test = new double[5];
//for (int i = 0; i < train.Length; i++)
//{
// train[i] = timeSeriGenerator.TimeSeri[i];
//}
//for (int i = train.Length, j = 0; i < timeSeriGenerator.TimeSeri.Length; i++, j++)
//{
// test[j] = timeSeriGenerator.TimeSeri[i];
//}
//ArimaGA aga=new ArimaGA();
//aga.StartArima(train);
//NumericalVariable timeSeriii = new NumericalVariable("timeSeriii", train);
//arimaModel = new ArimaModel(timeSeriii, aga.bestP, aga.bestD, aga.bestQ);
//arimaModel.Compute();
//var fv2 = arimaModel.Forecast(numberOfForecastTests);
//double ea2 = MyErrorParameters.ERROR_Percent(fv2.ToArray(), test);)
for (int i = 0; i < myTimeSeriForBestHybrid.part2.Count; i++)
{
StartArima(myTimeSeriForBestHybrid.part1.ToArray());
//// converting to double[]
//double[] db = new double[myTimeSeriForBestHybrid.part1.Count];
//for (int j = 0; j < db.Length; j++)
//{
// db[j] = myTimeSeriForBestHybrid.part1.ToArray()[j];
//}
NumericalVariable timeSerii = new NumericalVariable("timeSerii", myTimeSeriForBestHybrid.part1.ToArray());
arimaModel = new ArimaModel(timeSerii, myBestP, myBestD, myBestQ);
arimaModel.Compute();
var res = arimaModel.Forecast(1);
float lt = (float)res[0];
Lt.Add(lt);
double target = myTimeSeriForBestHybrid.part2[i];
double e = lt - target;
Et.Add(e);
myTimeSeriForBestHybrid.part1.Add(target);
double mu = myTimeSeriForBestHybrid.part1.Average();
if (myBestD == 0)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] - mu);
}
else if (myBestD == 1)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] - mu);
}
else
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
2 * myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] +
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 3] - mu);
}
}
ArimaModel EtArimaModel = new ArimaGA().GetBestModel(Et.ToArray());
ArimaModel ZtArimaModel = new ArimaGA().GetBestModel(Zt.ToArray());
int a = 0;
SVM svm = new SVM();
//TimeSeriGenerator<double> gen = new TimeSeriGenerator<double>();
//gen.NumberOfInputVariables = Int32.Parse(NumberOfInpTextBox.Text);
//gen.TimeSeri = Et.ToArray();
//var EtTimeSeries = gen.generate();
//gen = new TimeSeriGenerator<double>();
//gen.NumberOfInputVariables = Int32.Parse(NumberOfInpTextBox.Text);
//gen.TimeSeri = Zt.ToArray();
//var ZtTimeSeries = gen.generate();
//// biaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
Pair <int> bestAB = CreateComplexHybridModel(Et.ToArray(), Lt.ToArray(), Zt.ToArray());
double minErr = SVMComplexModelForBestModel(bestAB.First, bestAB.Second, Et.ToArray(), Lt.ToArray(),
Zt.ToArray());
MessageBox.Show(bestAB.First + " , " + bestAB.Second + "\nMinError In Training Is => " + minErr, "Now Best M & N Found", MessageBoxButton.OK,
MessageBoxImage.Asterisk);
// --------------------------------- now our complex hybrid model is created -----------------------------------------------------------------
double mse = 0;
double errorPercent = 0;
double sumTargets = 0;
if (myTimeSeriForBestHybrid.part1.Count != timeSeriGenerator.TimeSeri.Length - numberOfForecastTests)
{
MessageBox.Show("Input For Arima Model Is Not Completed", "ERROR", MessageBoxButton.OK, MessageBoxImage.Error);
}
// << CHECK HERE >> (FOR CHECKING PURPOSE ONLY , COMMENT HERE LATER)
var forecastedVector = arimaModel.Forecast(numberOfForecastTests);
double eoa = MyErrorParameters.ERROR_Percent(forecastedVector.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
MessageBox.Show("Error Of Arima Is => " + eoa, "Arima Error", MessageBoxButton.OK,
MessageBoxImage.Information);
//maxGAIteretionInArima = 1000;
//StartArima(myTimeSeriForBestHybrid.part1.ToArray());
//double[] dbb = new double[myTimeSeriForBestHybrid.part1.Count];
//for (int j = 0; j < dbb.Length; j++)
//{
// dbb[j] = myTimeSeriForBestHybrid.part1.ToArray()[j];
//}
//NumericalVariable timeSeriTest = new NumericalVariable("timeSerii", dbb);
//arimaModel = new ArimaModel(timeSeriTest, myBestP, myBestD, myBestQ);
//arimaModel.Compute();
StreamWriter hybridWriter = new StreamWriter(OUTPUT_FILE_NAME);
List <double> results = new List <double>();
//double errorOfArima = MyErrorParameters.ERROR_Percent(forcastedVector.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
//MessageBox.Show("Error Of Arima Is => " + errorOfArima, "Arima Error", MessageBoxButton.OK,
// MessageBoxImage.Information);
// ---------------------------------------------------------------
int numOfInp = bestAB.First + bestAB.Second + 1;
int rows = Et.Count - Math.Max(bestAB.First, bestAB.Second);
float[,] inps = new float[rows, numOfInp];
double[] targs = new double[rows];
int y = bestAB.First;
int z = bestAB.Second;
int ll = 0;
for (int o = 0; o < rows; o++)
{
if (y > z)
{
for (int j = 0; j < y; j++)
{
inps[o, j] = (float)Et[ll + j];
}
inps[o, y] = (float)Lt[ll + y];
for (int j = 0; j < z; j++)
{
inps[o, y + j + 1] = (float)Zt[ll + y - z + j];
}
targs[o] = timeSeriGenerator.TimeSeri[ll + y];
}
else
{
for (int j = 0; j < y; j++)
{
inps[o, j] = (float)Et[ll + z - y + j];
}
inps[o, y] = (float)Lt[ll + z];
for (int j = 0; j < z; j++)
{
inps[o, j + y + 1] = (float)Zt[ll + j];
}
targs[o] = timeSeriGenerator.TimeSeri[ll + z];
}
ll++;
}
float[,] trainInputs = new float[rows - numberOfTests, numOfInp];
float[] trainTargets = new float[rows - numberOfTests];
float[,] testInputs = new float[numberOfTests, numOfInp];
float[] testTargets = new float[numberOfTests];
int t = 0;
for (; t < rows - numberOfTests; t++)
{
for (int j = 0; j < numOfInp; j++)
{
trainInputs[t, j] = inps[t, j];
}
trainTargets[t] = (float)targs[t];
}
for (int o = 0; t < rows; o++, t++)
{
for (int j = 0; j < numOfInp; j++)
{
testInputs[o, j] = inps[t, j];
}
testTargets[o] = (float)targs[t];
}
svmModelHybrid = new SVM();
SVM_KERNEL_TYPE bestKernelType = SVM_KERNEL_TYPE.RBF;
double bestEps = 0.001;
SVMParams p;
Matrix <float> trainData = new Matrix <float>(trainInputs);
Matrix <float> trainClasses = new Matrix <float>(trainTargets);
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 = svmModelHybrid.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 10);
// ---------------------------------------------------------------
for (int i = 0; i < numberOfForecastTests; i++)
{
float[,] inpTest = new float[bestAB.First + bestAB.Second + 1, 1];
int l = 0;
for (int j = 0; j < bestAB.First; j++, l++)
{
inpTest[l, 0] = (float)Et[Et.Count - bestAB.First + j];
}
inpTest[l++, 0] = (float)forecastedVector[i];
for (int j = 0; j < bestAB.Second; j++, l++)
{
inpTest[l, 0] = (float)Zt[Zt.Count - bestAB.Second + j];
}
// injaaaaaaaaaaaaaaaaaaaa
float result = svmModelHybrid.Predict(new Matrix <float>(inpTest));
results.Add(result);
hybridWriter.WriteLine(result);
double target = myTimeSeriForBestHybrid.testCases[i];
// preparing for next use in this for loop
double resi = target - (float)forecastedVector[i]; // float resi = target - result; << CHECK HERE IMPORTANT >>
Et.Add(resi);
myTimeSeriForBestHybrid.part1.Add(target);
double mu = myTimeSeriForBestHybrid.part1.Average();
if (myBestD == 0)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] - mu);
}
else if (myBestD == 1)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] - mu);
}
else //else if (bestD == 2) << CHECK HERE >>
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
2 * myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] +
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 3] - mu);
}
}
double _mse = MyErrorParameters.MSE(results.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
double _errorPercent = MyErrorParameters.ERROR_Percent(results.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
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);
}