Line data    Source code

       1             : #ifndef ALINDLOCALREGRESSION_H
       2             : #define ALINDLOCALREGRESSION_H
       3             : /* Copyright(c) 2006-07, ALICE Experiment at CERN, All rights reserved. *
       4             :  * See cxx source for full Copyright notice                               */
       5             : 
       6             : #include "TGraph.h"
       7             : #include "TGraphSmooth.h"
       8             : #include "TRandom.h"
       9             : #include "TSpline.h"
      10             : #include "TLinearFitter.h"
      11             : #include "TDecompSVD.h"
      12             : #include "TDecompSparse.h"
      13             : #include "TMatrixDSparse.h"
      14             : #include "TF1.h"
      15             : #include "TH1F.h"
      16             : #include "TNamed.h"
      17             : #include "TClonesArray.h"
      18             : #include "TMath.h"
      19             : 
      20             : 
      21             : #include "TTreeStream.h"
      22             : class TTreeSRedirector;
      23             : class THn;
      24             : class TObjString; 
      25             : 
      26             : class AliNDLocalRegression : public TNamed {
      27             :  public:
      28             :   AliNDLocalRegression();
      29             :   AliNDLocalRegression(const char* name, const char* title);
      30             :   ~AliNDLocalRegression();
      31             : 
      32             :   Bool_t MakeFit(TTree * tree , const char *formulaVal, const char * formulaVar, const char*selection, const char * formulaKernel,  const char * dimensionFormula, Double_t weightCut=0.00001, Int_t entries=1000000000, Bool_t useBinNorm=kTRUE);
      33             :   Bool_t   CleanCovariance();
      34             :   Double_t Eval(Double_t *point);
      35             :   Double_t EvalError(Double_t *point);
      36             :   Bool_t   Derivative(Double_t *point, Double_t *d);
      37             :   Bool_t   EvalAndDerivative(Double_t *point, Double_t &val, Double_t *d);
      38           0 :   const THn *GetHistogram() {return fHistPoints;}
      39           0 :   const TObjArray *   GetFitParam(){ return fLocalFitParam;}
      40             :   void SetCuts(Double_t nSigma=6, Double_t robustFraction=0.95, Int_t estimator=1);
      41             :   void SetHistogram(THn* histo );
      42           0 :   void SetTree(TTree * tree) {fInputTree = tree;}
      43           0 :   TTreeSRedirector *GetStreamer(){return fStreamer;}
      44           0 :   void SetStreamer( TTreeSRedirector *streamer){ fStreamer=streamer;}
      45             :   Bool_t AddWeekConstrainsAtBoundaries(Int_t nDims, Int_t *indexes, Double_t *relWeight, TTreeSRedirector* pcstream, Bool_t useCommon=kFALSE);
      46             :   void DumpToTree(Int_t div, TTreeStream & stream);
      47             :   //
      48           0 :   const TObjArray *GetLocalFitParam()   const { return fLocalFitParam;   }
      49           0 :   const TObjArray *GetLocalFitQuality() const { return fLocalFitQuality; }
      50           0 :   const TObjArray *GetLocalFitCovar()   const { return fLocalFitCovar;   }
      51             :   //
      52           0 :   const TMatrixD  *GetLocalRobustStat() const { return fLocalRobustStat; }
      53             :   //
      54             :   // function to access the Local Regression from the TFormula
      55             :   static void AddVisualCorrection(AliNDLocalRegression* corr, Int_t position=0);
      56             :   static AliNDLocalRegression*  GetVisualCorrection(Int_t position);
      57           0 :   static AliNDLocalRegression*  GetVisualCorrection(const char *corName){return (fgVisualCorrection==NULL) ? 0: ( AliNDLocalRegression*) fgVisualCorrection->FindObject(corName);}
      58             :   static Int_t GetVisualCorrectionIndex(const char *corName);
      59           0 :   Int_t GetVisualCorrectionIndex(){return GetVisualCorrectionIndex(GetName());}
      60           0 :   static TObjArray*  GetVisualCorrections() { return fgVisualCorrection;}
      61             :   //
      62             :   static Double_t GetCorrND(Double_t index, Double_t par0);
      63             :   static Double_t GetCorrNDError(Double_t index, Double_t par0);
      64             :   static Double_t GetCorrND(Double_t index, Double_t par0,Double_t par1);
      65             :   static Double_t GetCorrNDError(Double_t index, Double_t par0,Double_t par1);
      66             :   static Double_t GetCorrND(Double_t index, Double_t par0,Double_t par1, Double_t par2);
      67             :   static Double_t GetCorrNDError(Double_t index, Double_t par0,Double_t par1, Double_t par2);
      68             :   static Double_t GetCorrND(Double_t index, Double_t par0,Double_t par1, Double_t par2, Double_t par3);
      69             :   static Double_t GetCorrNDError(Double_t index, Double_t par0,Double_t par1, Double_t par2, Double_t par3);
      70           0 :   static void SetVerboseLevel(Int_t verbose){ fgVerboseLevel=TMath::Max(verbose,1);}
      71             :   //
      72             :   static Double_t EvalGraphKernel(TGraph * gr, Double_t evalTime, Double_t kernelWidth, Int_t sigmaCut=3, Bool_t evalLog=kFALSE, Int_t pol=1, TVectorD *param=0, TMatrixD *covar=0); 
      73             :  public:
      74             :   Bool_t MakeRobustStatistic(TVectorD &values,TVectorD &errors,  TObjArray &pointArray,  TObjArray &kernelArrayI2, Double_t weightCut, Double_t robustFraction);
      75             : 
      76             : protected:
      77             :   THn *fHistPoints;                     //  histogram local point distoribution
      78             :   Double_t fRobustFractionLTS;          //  fraction of data used for the robust mean and robust rms estimator (LTS https://en.wikipedia.org/wiki/Least_trimmed_squares)
      79             :   Double_t fRobustRMSLTSCut;            //  cut on the robust RMS  |value-localmean|<fRobustRMSLTSCut*localRMS
      80             :   Int_t    fCutType;                    //  type of the cut 0- no cut 1-cut localmean=median, 2-cut localmen=rosbut mean 
      81             :   TTree * fInputTree;                 // ! input tree - object not owner  
      82             :   TTreeSRedirector * fStreamer;       // ! streamer to keep - test intermediate data
      83             :   //
      84             :   TObjString *fFormulaVal;            // value:error definition formula
      85             :   TObjString *fSelection;             // point selector formula
      86             :   //
      87             :   TObjString *fFormulaVar;            // :separated variable definition formula
      88             :   TObjString *fKernelWidthFormula;    //: separated - kernel width for the regression
      89             :   TObjString *fPolDimensionFormula;   //: separated  - polynom for the regression
      90             :   //
      91             :   Int_t fNVar;                        // number of variables
      92             :   Int_t fNParameters;                 // number of local parameters to fit
      93             :   //
      94             :   TObjArray *fLocalFitParam;          // local fit parameters + RMS + chi2
      95             :   TObjArray *fLocalFitQuality;        // local fit npoints chi2
      96             :   TObjArray *fLocalFitCovar;          // local fit covariance matrix  
      97             :   //
      98             :   TMatrixD  *fLocalRobustStat;        // local robust statistic
      99             : protected:
     100             :   static TObjArray *fgVisualCorrection; ///< array of orrection for visualization
     101             :   static Int_t fgVerboseLevel;          ///< verbose level
     102             :   Int_t    *fBinIndex;                  //[fNParameters] working arrays current bin index
     103             :   Double_t *fBinCenter;                 //[fNParameters] working current local variables - bin center
     104             :   Double_t *fBinDelta;                  //[fNParameters] working current local variables - bin delta
     105             :   Double_t *fBinWidth;                  //[fNParameters] working current local variables - bin delta
     106             :   Bool_t   fUseBinNorm;                 //  switch make polynom  in units of bins (kTRUE)  or  in natural units (kFALSE)
     107             : private:  
     108             :   AliNDLocalRegression& operator=(const AliNDLocalRegression&);
     109             :   AliNDLocalRegression(const AliNDLocalRegression&);
     110         128 :   ClassDef(AliNDLocalRegression, 2);
     111             : };
     112             : #endif