TFumili.h

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// @(#)root/fumili:$Id$
// Author: Rene Brun   31/08/99

#ifndef ROOT_TFumili
#define ROOT_TFumili

#include "TVirtualFitter.h"

class TF1;

class TFumili : public  TVirtualFitter {
private:
   Int_t fMaxParam;       ///<
   Int_t fNlog;           ///<
   Int_t fNfcn;           ///< Number of FCN calls;
   Int_t fNED1;           ///< Number of experimental vectors X=(x1,x2,...xK)
   Int_t fNED2;           ///< K - Length of vector X plus 2 (for chi2)
   Int_t fNED12;          ///< fNED1+fNED2
   Int_t fNpar;           ///<  fNpar - number of parameters
   Int_t fNstepDec;       ///<  fNstepDec - maximum number of step decreasing counter
   Int_t fNlimMul;        ///<  fNlimMul - after fNlimMul successful iterations permits four-fold increasing of fPL
   Int_t fNmaxIter;       ///<  fNmaxIter - maximum number of iterations
   Int_t fLastFixed;      ///< Last fixed parameter number
   Int_t fENDFLG;         ///< End flag of fit
   Int_t fINDFLG[5];      ///< internal flags;


   Bool_t fGRAD;          ///< user calculated gradients
   Bool_t fWARN;          ///< warnings
   Bool_t fDEBUG;         ///< debug info
   Bool_t fLogLike;       ///< LogLikelihood flag
   Bool_t fNumericDerivatives; ///<

   Double_t *fZ0;         ///< [fMaxParam2] Matrix of approximate second derivatives of objective function
                          ///< This matrix is diagonal and always contain only variable parameter's
                          ///< derivatives
   Double_t *fZ;          ///< [fMaxParam2] Invers fZ0 matrix - covariance matrix
   Double_t *fGr;         ///< [fMaxParam] Gradients of objective function
   Double_t *fParamError; ///< [fMaxParam] Parameter errors
   Double_t *fSumLog;     ///< [fNlog]
   Double_t *fEXDA;       ///< [fNED12] experimental data poInt_ter

   //  don't calculate parameter errors - take them from fParamError array
   Double_t *fA;          ///< [fMaxParam] Fit parameter array
   Double_t *fPL0;        ///< [fMaxParam] Step initial bounds
   Double_t *fPL;         ///< [fMaxParam] Limits for parameters step. If <0, then parameter is fixed

   // Defines multidimensional parallelepiped with center in param. vector
   Double_t *fDA;         ///< [fMaxParam] Parameter step
   Double_t *fAMX;        ///< [fMaxParam] Maximum param value
   Double_t *fAMN;        ///< [fMaxParam] Minimum param value
   Double_t *fR;          ///< [fMaxParam] Correlation factors

   Double_t *fDF;         ///< [fMaxParam] First derivatives of theoretical function
   Double_t *fCmPar;      ///< [fMaxParam] parameters of commands

   Double_t fS;           ///<  fS - objective function value (return)
   Double_t fEPS;         ///<  fEPS - required precision of parameters. If fEPS<0 then
   Double_t fRP;          ///< Precision of fit ( machine zero on CDC 6000) quite old yeh?
   Double_t fAKAPPA;      ///<
   Double_t fGT;          ///< Expected function change in next iteration
   TString *fANames;      ///< [fMaxParam] Parameter names
   TString fCword;        ///<  Command string


//  TF1 *fTFNF1;         //Pointer to theoretical function
//  void (*fFCN) (Int_t &, Double_t *, Double_t &f, Double_t *, Int_t); //
//  //wrapper function to calculate functional value, gradients and Z-matrix
//  Double_t (*fTFN)(Double_t *, Double_t *, Double_t*); // Wrapper function for TFN

public:

   TFumili(Int_t maxpar=25);
   virtual  ~TFumili();

   void             BuildArrays();
   virtual Double_t Chisquare(Int_t npar, Double_t *params) const;
   virtual void     Clear(Option_t *opt="");
   void             DeleteArrays();
   void             Derivatives(Double_t*,Double_t*);
   Int_t            Eval(Int_t& npar, Double_t *grad, Double_t &fval, Double_t *par, Int_t flag); // Evaluate the minimisation function
   Double_t         EvalTFN(Double_t *,Double_t*);
   virtual Int_t    ExecuteCommand(const char *command, Double_t *args, Int_t nargs);
   Int_t            ExecuteSetCommand(Int_t );
   virtual void     FitChisquare(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
   virtual void     FitChisquareI(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
   virtual void     FitLikelihood(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
   virtual void     FitLikelihoodI(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
   virtual void     FixParameter(Int_t ipar);
   virtual Double_t *GetCovarianceMatrix() const;
   virtual Double_t GetCovarianceMatrixElement(Int_t i, Int_t j) const;
   virtual Int_t    GetErrors(Int_t ipar,Double_t &eplus, Double_t &eminus, Double_t &eparab, Double_t &globcc) const;
   virtual Int_t    GetNumberTotalParameters() const;
   virtual Int_t    GetNumberFreeParameters() const;
   Double_t*        GetPL0() const { return fPL0;}
   virtual Double_t GetParError(Int_t ipar) const;
   virtual Double_t GetParameter(Int_t ipar) const ;
   virtual Int_t    GetParameter(Int_t ipar,char *name,Double_t &value,Double_t &verr,Double_t &vlow, Double_t &vhigh) const;
   virtual const char *GetParName(Int_t ipar) const;
   virtual Int_t    GetStats(Double_t &amin, Double_t &edm, Double_t &errdef, Int_t &nvpar, Int_t &nparx) const;
   virtual Double_t GetSumLog(Int_t );
   Double_t*        GetZ() const { return fZ;}
   void             InvertZ(Int_t);
   virtual Bool_t   IsFixed(Int_t ipar) const;
   Int_t            Minimize();
   virtual void     PrintResults(Int_t k,Double_t p) const;
   virtual void     ReleaseParameter(Int_t ipar);
   Int_t            SGZ();
   void             SetData(Double_t *,Int_t,Int_t);
   virtual void     SetFitMethod(const char *name);
   virtual Int_t    SetParameter(Int_t ipar,const char *parname,Double_t value,Double_t verr,Double_t vlow, Double_t vhigh);
   void             SetParNumber(Int_t ParNum) { fNpar = ParNum;};

   ClassDef(TFumili,0) //The FUMILI Minimization package
};

R__EXTERN TFumili * gFumili;
#endif