30#ifndef ROOT_TMVA_MethodLD
31#define ROOT_TMVA_MethodLD
58 const TString& theOption =
"LD");
#define ClassDef(name, id)
Class that contains all the data information.
Virtual base Class for all MVA method.
virtual void ReadWeightsFromStream(std::istream &)=0
void GetSum(void)
Calculates the matrix transposed(X)*W*X with W being the diagonal weight matrix and X the coordinates...
void GetHelpMessage() const
get help message text
const Ranking * CreateRanking()
computes ranking of input variables
Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
LD can handle classification with 2 classes and regression with one regression-target.
MethodLD(const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="LD")
standard constructor for the LD
std::vector< std::vector< Double_t > * > * fLDCoeff
void DeclareOptions()
MethodLD options.
void GetLDCoeff(void)
Calculates the coefficients used for classification/regression.
virtual const std::vector< Float_t > & GetRegressionValues()
Calculates the regression output.
void ReadWeightsFromStream(std::istream &i)
read LD coefficients from weight file
void ReadWeightsFromXML(void *wghtnode)
read coefficients from xml weight file
void Init(void)
default initialization called by all constructors
void ProcessOptions()
this is the preparation for training
void PrintCoefficients(void)
Display the classification/regression coefficients for each variable.
void Train(void)
compute fSumMatx
virtual ~MethodLD(void)
destructor
void AddWeightsXMLTo(void *parent) const
create XML description for LD classification and regression (for arbitrary number of output classes/t...
void MakeClassSpecific(std::ostream &, const TString &) const
write LD-specific classifier response
void InitMatrices(void)
Initialization method; creates global matrices and vectors.
Double_t GetMvaValue(Double_t *err=0, Double_t *errUpper=0)
Returns the MVA classification output.
void GetSumVal(void)
Calculates the vector transposed(X)*W*Y with Y being the target vector.
Ranking for variables in method (implementation)
Abstract ClassifierFactory template that handles arbitrary types.