int main ()
{
// Make a new OnlineSVR
OnlineSVR* SVR = new OnlineSVR();
// Set parameters
SVR->SetC(1);
SVR->SetEpsilon(0.01);
SVR->SetKernelType(OnlineSVR::KERNEL_RBF);
SVR->SetKernelParam(30);
SVR->SetVerbosity(OnlineSVR::VERBOSITY_NORMAL);
// Build training set
Matrix<double>* TrainingSetX = Matrix<double>::RandMatrix(20,1);
Vector<double>* TrainingSetY = new Vector<double>();
for (int i=0; i<TrainingSetX->GetLengthRows(); i++)
TrainingSetY->Add(sin(TrainingSetX->GetValue(i,0)));
// Train OnlineSVR
SVR->Train(TrainingSetX,TrainingSetY);
// Show OnlineSVR info
SVR->ShowInfo();
// Predict some new values
Matrix<double>* TestSetX = new Matrix<double>();
Vector<double>* X1 = new Vector<double>();
Vector<double>* X2 = new Vector<double>();
X1->Add(0);
X2->Add(1);
TestSetX->AddRowRef(X1);
TestSetX->AddRowRef(X2);
Vector<double>* PredictedY = SVR->Predict(TestSetX);
cout << "f(0) = " << PredictedY->GetValue(0) << endl;
cout << "f(1) = " << PredictedY->GetValue(1) << endl;
// Forget some samples
Vector<int>* RemainingSamples = SVR->GetRemainingSetIndexes()->Clone();
SVR->Forget(RemainingSamples);
// Save OnlineSVR
SVR->SaveOnlineSVR("Sin.svr");
// Delete
delete SVR;
delete TrainingSetX;
delete TrainingSetY;
delete TestSetX;
delete PredictedY;
delete RemainingSamples;
return 0;
}
