// 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 StartSVM(float[] data)
{
timeSeriGenerator = new TimeSeriGenerator <float>();
MyTimeSeri <float> myTimeSeri = timeSeriGenerator.generateWithThisData(data, 6);
Matrix <float> trainData = new Matrix <float>(myTimeSeri.inputs);
Matrix <float> trainClasses = new Matrix <float>(myTimeSeri.targets);
svmModel = new SVM();
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.POLY;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.EPS_SVR; // for regression
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
p.Gamma = 1;
p.Degree = 1;
p.P = 1;
p.Nu = 0.1;
bool trained = svmModel.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 10);
}
