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;
}
int main (int argc, char* argv[])
{
// Default Parameters
bool NewOnlineSVR = true;
int LearningType = -1;
char InputFile[256] = "";
char OutputFile[256] = "";
char DataFile[256] = "";
char TestFile[256] = "";
double C = 30;
double Epsilon = 0.1;
int KernelType = OnlineSVR::KERNEL_RBF;
double KernelParam = 30;
double KernelParam2 = 0;
double ErrorTollerance = Epsilon/10;
int Verbosity = 1;
bool StabilizedLearning = true;
bool SaveKernelMatrix = true;
int TrainingSize = 30;
int TestSize = 1;
bool ChangedC = false;
bool ChangedEpsilon = false;
bool ChangedKernelType = false;
bool ChangedKernelParam = false;
bool ChangedKernelParam2 = false;
bool ChangedErrorTollerance = false;
bool ChangedVerbosity = false;
bool ChangedStabilizedLearning = false;
bool ChangedSaveKernelMatrix = false;
bool ChangedAnalyzeLearning = false;
bool ChangedOnlineLearning = false;
// Check Wrong Params
if (argc==1) {
//cerr << "Parameters not valid." << endl;
ShowUsage();
exit(0);
}
// Load Params
for (int i=1; i<argc; i++) {
// Train
if (stricmp(argv[i],"-Train")==0) {
LearningType = OnlineSVR::OPERATION_LEARNING;
//cout << "Operation = Train" << endl;
}
// Forget
else if (stricmp(argv[i],"-Forget")==0) {
LearningType = OnlineSVR::OPERATION_UNLEARNING;
//cout << "Operation = Forgetting" << endl;
}
// Stabilize
else if (stricmp(argv[i],"-Stabilize")==0) {
LearningType = OnlineSVR::OPERATION_STABILIZING;
//cout << "Operation = Stabilizing" << endl;
}
// Predict
else if (stricmp(argv[i],"-Predict")==0) {
LearningType = OnlineSVR::OPERATION_PREDICT;
//cout << "Operation = Predict" << endl;
}
// Margin
else if (stricmp(argv[i],"-Margin")==0) {
LearningType = OnlineSVR::OPERATION_MARGIN;
//cout << "Operation = Margin" << endl;
}
// C
else if (stricmp(argv[i],"-C")==0) {
float FloatC = 0;
sscanf (argv[++i], "%f", &FloatC);
C = static_cast<double>(FloatC);
ChangedC = true;
//cout << "C = " << C << endl;
}
// Epsilon
else if (stricmp(argv[i],"-Epsilon")==0) {
float FloatEpsilon = 0;
sscanf (argv[++i], "%f", &FloatEpsilon);
Epsilon = static_cast<double>(FloatEpsilon);
ChangedEpsilon = true;
//cout << "Epsilon = " << Epsilon << endl;
}
// KernelType
else if (stricmp(argv[i],"-KernelType")==0) {
i++;
if (stricmp(argv[i],"Linear")==0)
KernelType = OnlineSVR::KERNEL_LINEAR;
else if (stricmp(argv[i],"Polynomial")==0)
KernelType = OnlineSVR::KERNEL_POLYNOMIAL;
else if (stricmp(argv[i],"RBF")==0)
KernelType = OnlineSVR::KERNEL_RBF;
else if (stricmp(argv[i],"GaussianRBF")==0)
KernelType = OnlineSVR::KERNEL_RBF_GAUSSIAN;
else if (stricmp(argv[i],"ExponentialRBF")==0)
KernelType = OnlineSVR::KERNEL_RBF_EXPONENTIAL;
else if (stricmp(argv[i],"MLP")==0)
KernelType = OnlineSVR::KERNEL_MLP;
else {
cerr << "Error: Kernel Type not valid!!" << endl;
exit (1);
}
ChangedKernelType = true;
}
// KernelParam
else if (stricmp(argv[i],"-KernelParam")==0) {
float FloatKernelParam = 0;
sscanf (argv[++i], "%f", &FloatKernelParam);
KernelParam = static_cast<double>(FloatKernelParam);
ChangedKernelParam = true;
//cout << "KernelParam = " << KernelParam << endl;
}
// KernelParam 2
else if (stricmp(argv[i],"-KernelParam2")==0) {
float FloatKernelParam2 = 0;
sscanf (argv[++i], "%f", &FloatKernelParam2);
KernelParam2 = static_cast<double>(FloatKernelParam2);
ChangedKernelParam2 = true;
//cout << "KernelParam2 = " << KernelParam2 << endl;
}
// Error Tollerance
else if (stricmp(argv[i],"-ErrorTollerance")==0) {
float FloatErrorTollerance = 0;
sscanf (argv[++i], "%f", &FloatErrorTollerance);
ErrorTollerance = static_cast<double>(FloatErrorTollerance);
ChangedErrorTollerance = true;
//cout << "ErrorTollerance = " << ErrorTollerance << endl;
}
// Verbosity
else if (stricmp(argv[i],"-Verbosity")==0) {
sscanf (argv[++i], "%d", &Verbosity);
ChangedVerbosity = true;
//cout << "Verbosity = " << Verbosity << endl;
}
// SaveKernelMatrix
else if (stricmp(argv[i],"-SaveKernelMatrix")==0) {
SaveKernelMatrix = true;
ChangedSaveKernelMatrix = true;
//cout << "Save the Kernel Matrix" << endl;
}
// NotSaveKernelMatrix
else if (stricmp(argv[i],"-NotSaveKernelMatrix")==0) {
SaveKernelMatrix = false;
ChangedSaveKernelMatrix = true;
//cout << "Don't save the Kernel Matrix" << endl;
}
// StabilizedLearning
else if (stricmp(argv[i],"-StabilizedLearning")==0) {
StabilizedLearning = true;
ChangedStabilizedLearning = true;
//cout << "Stabilized Learning" << endl;
}
// NotStabilizedLearning
else if (stricmp(argv[i],"-NotStabilizedLearning")==0) {
StabilizedLearning = false;
ChangedStabilizedLearning = true;
//cout << "Not Stabilized Learning" << endl;
}
// Load
else if (stricmp(argv[i],"-Load")==0) {
NewOnlineSVR = false;
sscanf (argv[++i], "%s", InputFile);
//cout << "Input = " << InputFile << endl;
}
// Save
else if (stricmp(argv[i],"-Save")==0) {
sscanf (argv[++i], "%s", OutputFile);
//cout << "Output = " << OutputFile << endl;
}
// Data
else if (stricmp(argv[i],"-Data")==0) {
sscanf (argv[++i], "%s", DataFile);
//cout << "Data = " << DataFile << endl;
}
// AnalyzeLearning
else if (stricmp(argv[i],"-AnalyzeLearning")==0) {
sscanf (argv[++i], "%s", TestFile);
ChangedAnalyzeLearning = true;
}
// OnlineLearning
else if (stricmp(argv[i],"-OnlineLearning")==0) {
ChangedOnlineLearning = true;
}
// TrainingSize
else if (stricmp(argv[i],"-TrainingSize")==0) {
sscanf (argv[++i], "%d", &TrainingSize);
}
// TestSize
else if (stricmp(argv[i],"-TestSize")==0) {
sscanf (argv[++i], "%d", &TestSize);
}
// Error
else {
cerr << "Error! Parameter Not Valid!" << endl;
exit (1);
}
}
// Build OnlineSVR
OnlineSVR* SVR = new OnlineSVR();
if (!NewOnlineSVR)
SVR->LoadOnlineSVR(InputFile);
if (ChangedC)
SVR->SetC(C);
if (ChangedEpsilon)
SVR->SetEpsilon(Epsilon);
if (ChangedKernelType)
SVR->SetKernelType(KernelType);
if (ChangedKernelParam)
SVR->SetKernelParam(KernelParam);
if (ChangedKernelParam2)
SVR->SetKernelParam2(KernelParam2);
if (ChangedErrorTollerance) {
SVR->SetAutoErrorTollerance(false);
SVR->SetErrorTollerance(ErrorTollerance);
}
if (ChangedVerbosity)
SVR->SetVerbosity(Verbosity);
if (ChangedStabilizedLearning)
SVR->SetStabilizedLearning(StabilizedLearning);
if (ChangedSaveKernelMatrix)
SVR->SetSaveKernelMatrix(SaveKernelMatrix);
if (LearningType==OnlineSVR::OPERATION_LEARNING) {
if (ChangedAnalyzeLearning) {
// Analyze Training
Matrix<double>* X = NULL;
Vector<double>* Y = NULL;
Matrix<double>* TestSetX = NULL;
Vector<double>* TestSetY = NULL;
SVR->Import(DataFile, &X, &Y);
SVR->Import(TestFile, &TestSetX, &TestSetY);
SVR->Train(X,Y,TestSetX,TestSetY);
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","OnlineSVR1.svr");
SVR->SaveOnlineSVR(OutputFile);
delete X;
delete Y;
delete TestSetX;
delete TestSetY;
delete SVR;
} else if (ChangedOnlineLearning) {
// Online Training
Matrix<double>* X = NULL;
Vector<double>* Y = NULL;
SVR->Import(DataFile, &X, &Y);
SVR->Train(X,Y,TrainingSize,TestSize);
delete X;
delete Y;
delete SVR;
} else {
// Normal Training
Matrix<double>* X = NULL;
Vector<double>* Y = NULL;
SVR->Import(DataFile, &X, &Y);
SVR->Train(X,Y);
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","OnlineSVR1.svr");
SVR->SaveOnlineSVR(OutputFile);
delete X;
delete Y;
delete SVR;
}
}
else if (LearningType==OnlineSVR::OPERATION_UNLEARNING) {
Vector<int>* Indexes = Indexes->Load(DataFile);
SVR->Forget(Indexes);
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","OnlineSVR1.svr");
SVR->SaveOnlineSVR(OutputFile);
delete Indexes;
delete SVR;
} else if (LearningType==OnlineSVR::OPERATION_STABILIZING) {
int StabilizationNumber = 0;
while (!SVR->VerifyKKTConditions()) {
SVR->Stabilize();
StabilizationNumber ++;
if (StabilizationNumber>SVR->GetSamplesTrainedNumber()) {
cerr << "Error: it's impossible to stabilize the OnlineSVR. Please add or remove some samples." << endl;
break;
}
}
if (SVR->GetVerbosity()>=3)
SVR->ShowDetails();
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","OnlineSVR1.svr");
SVR->SaveOnlineSVR(OutputFile);
delete SVR;
} else if (LearningType==OnlineSVR::OPERATION_PREDICT) {
Matrix<double>* X = X->Load(DataFile);
Vector<double>* Y = SVR->Predict(X);
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","Predict.txt");
Y->Save(OutputFile);
delete X;
delete Y;
delete SVR;
} else if (LearningType==OnlineSVR::OPERATION_MARGIN) {
Matrix<double>* X = NULL;
Vector<double>* Y = NULL;
SVR->Import(DataFile, &X, &Y);
Vector<double>* Margin = SVR->Margin(X,Y);
if (strlen(OutputFile)==0)
sprintf(OutputFile,"%s","Margin.txt");
Margin->Save(OutputFile);
delete X;
delete Y;
delete Margin;
delete SVR;
} else {
cerr << "Error! Operation not definited!" << endl;
exit (1);
}
return 0;
}
