// constructor
ReadMLP( std::vector<std::string>& theInputVars )
: IClassifierReader(),
fClassName( "ReadMLP" ),
fNvars( 5 ),
fIsNormalised( false )
{
// the training input variables
const char* inputVars[] = { "f_Z1mass", "f_Z2mass", "f_pt4l", "f_massjj", "f_deltajj" };
// sanity checks
if (theInputVars.size() <= 0) {
std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
fStatusIsClean = false;
}
if (theInputVars.size() != fNvars) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
<< theInputVars.size() << " != " << fNvars << std::endl;
fStatusIsClean = false;
}
// validate input variables
for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
if (theInputVars[ivar] != inputVars[ivar]) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
<< " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
fStatusIsClean = false;
}
}
// initialize min and max vectors (for normalisation)
fVmin[0] = -1;
fVmax[0] = 1;
fVmin[1] = -1;
fVmax[1] = 1;
fVmin[2] = -1;
fVmax[2] = 1;
fVmin[3] = -1;
fVmax[3] = 1;
fVmin[4] = -1;
fVmax[4] = 1;
// initialize input variable types
fType[0] = 'F';
fType[1] = 'F';
fType[2] = 'F';
fType[3] = 'F';
fType[4] = 'F';
// initialize constants
Initialize();
// initialize transformation
InitTransform();
}
Tracter::BSAPITransform::BSAPITransform(Component<float>* iInput, const char* iObjectName)
{
mObjectName = iObjectName;
inputdim = iInput->Frame().size;
Connect(iInput, 1);
mInput = iInput;
mInputID = NULL;
InitTransform();
InitOutBuffer();
LastFrameProcess=0;
}
Tracter::BSAPITransform::BSAPITransform(Component<float>* iInput, Component<float>* iInputID, const char* iObjectName)
{
mObjectName = iObjectName;
inputdim = iInput->Frame().size;
Connect(iInput, 1);
Connect(iInputID, 1);
mInput = iInput;
mInputID = iInputID;
const char* xformdir = GetEnv("MACRODIR", "");
mInputID_macroname_full = new char[strlen(xformdir) + sizeof(float)]; //Dir length + filename
strcpy(mInputID_macroname_full,xformdir);
InitTransform();
InitOutBuffer();
LastFrameProcess=0;
}
// constructor
ReadMLP_ANN_N2_NC800( std::vector<std::string>& theInputVars )
: IClassifierReader(),
fClassName( "ReadMLP_ANN_N2_NC800" ),
fNvars( 11 ),
fIsNormalised( false )
{
// the training input variables
const char* inputVars[] = { "Jet1Pt", "LepPt", "LepChg", "LepEta", "Met", "mt", "HT20", "NbLoose30", "Njet", "JetHBpt", "DrJetHBLep" };
// sanity checks
if (theInputVars.size() <= 0) {
std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
fStatusIsClean = false;
}
if (theInputVars.size() != fNvars) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
<< theInputVars.size() << " != " << fNvars << std::endl;
fStatusIsClean = false;
}
// validate input variables
for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
if (theInputVars[ivar] != inputVars[ivar]) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
<< " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
fStatusIsClean = false;
}
}
// initialize min and max vectors (for normalisation)
fVmin[0] = -1;
fVmax[0] = 1;
fVmin[1] = -1;
fVmax[1] = 0.99999988079071;
fVmin[2] = -1;
fVmax[2] = 1;
fVmin[3] = -1;
fVmax[3] = 1;
fVmin[4] = -1;
fVmax[4] = 1;
fVmin[5] = -1;
fVmax[5] = 1;
fVmin[6] = -1;
fVmax[6] = 1;
fVmin[7] = -1;
fVmax[7] = 1;
fVmin[8] = -1;
fVmax[8] = 1;
fVmin[9] = -1;
fVmax[9] = 1;
fVmin[10] = -1;
fVmax[10] = 1;
// initialize input variable types
fType[0] = 'F';
fType[1] = 'F';
fType[2] = 'F';
fType[3] = 'F';
fType[4] = 'F';
fType[5] = 'F';
fType[6] = 'F';
fType[7] = 'F';
fType[8] = 'F';
fType[9] = 'F';
fType[10] = 'F';
// initialize constants
Initialize();
// initialize transformation
InitTransform();
}
// constructor
ReadMLP( std::vector<std::string>& theInputVars )
: IClassifierReader(),
fClassName( "ReadMLP" ),
fNvars( 11 ),
fIsNormalised( false )
{
// the training input variables
const char* inputVars[] = { "dR_l1l2", "dR_b1b2", "dR_bl", "dR_l1l2b1b2", "MINdR_bl", "dphi_l1l2b1b2", "mass_l1l2", "mass_b1b2", "mass_trans", "MT2", "pt_b1b2" };
// sanity checks
if (theInputVars.size() <= 0) {
std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
fStatusIsClean = false;
}
if (theInputVars.size() != fNvars) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
<< theInputVars.size() << " != " << fNvars << std::endl;
fStatusIsClean = false;
}
// validate input variables
for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
if (theInputVars[ivar] != inputVars[ivar]) {
std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
<< " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
fStatusIsClean = false;
}
}
// initialize min and max vectors (for normalisation)
fVmin[0] = -1;
fVmax[0] = 1;
fVmin[1] = -1;
fVmax[1] = 1;
fVmin[2] = -1;
fVmax[2] = 1;
fVmin[3] = -1;
fVmax[3] = 1;
fVmin[4] = -1;
fVmax[4] = 1;
fVmin[5] = -1;
fVmax[5] = 1;
fVmin[6] = -1;
fVmax[6] = 0.99999988079071;
fVmin[7] = -1;
fVmax[7] = 1;
fVmin[8] = -1;
fVmax[8] = 0.99999988079071;
fVmin[9] = -1;
fVmax[9] = 1;
fVmin[10] = -1;
fVmax[10] = 1;
// initialize input variable types
fType[0] = 'F';
fType[1] = 'F';
fType[2] = 'F';
fType[3] = 'F';
fType[4] = 'F';
fType[5] = 'F';
fType[6] = 'F';
fType[7] = 'F';
fType[8] = 'F';
fType[9] = 'F';
fType[10] = 'F';
// initialize constants
Initialize();
// initialize transformation
InitTransform();
}
