TFitter*¶
The ROOT standard fitter based on TMinuit
TFitter 继承 TVirtualFitter
class¶
TFitter(Int_t maxpar = 25);
virtual ~TFitter();
virtual Double_t Chisquare(Int_t npar, Double_t *params) const ;/// return a chisquare equivalent
virtual void Clear(Option_t *option="");/// reset the fitter environment
virtual Int_t ExecuteCommand(const char *command, Double_t *args, Int_t nargs);
/// Execute a fitter command;
/// command : command string
/// args : list of nargs command arguments
virtual void FitChisquare(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
/// Minimization function for H1s using a Chisquare method
/// Default method (function evaluated at center of bin)
/// for each point the cache contains the following info
/// -1D : bc,e, xc (bin content, error, x of center of bin)
/// -2D : bc,e, xc,yc
/// -3D : bc,e, xc,yc,zc
virtual void FitChisquareI(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
/// Minimization function for H1s using a Chisquare method
/// The "I"ntegral method is used
/// for each point the cache contains the following info
/// -1D : bc,e, xc,xw (bin content, error, x of center of bin, x bin width of bin)
/// -2D : bc,e, xc,xw,yc,yw
/// -3D : bc,e, xc,xw,yc,yw,zc,zw
virtual void FitLikelihood(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
/// Minimization function for H1s using a Likelihood method*-*-*-*-*-*
/// Basically, it forms the likelihood by determining the Poisson
/// probability that given a number of entries in a particular bin,
/// the fit would predict it's value. This is then done for each bin,
/// and the sum of the logs is taken as the likelihood.
/// Default method (function evaluated at center of bin)
/// for each point the cache contains the following info
/// -1D : bc,e, xc (bin content, error, x of center of bin)
/// -2D : bc,e, xc,yc
/// -3D : bc,e, xc,yc,zc
virtual void FitLikelihoodI(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag);
/// Minimization function for H1s using a Likelihood method*-*-*-*-*-*
/// Basically, it forms the likelihood by determining the Poisson
/// probability that given a number of entries in a particular bin,
/// the fit would predict it's value. This is then done for each bin,
/// and the sum of the logs is taken as the likelihood.
/// The "I"ntegral method is used
/// for each point the cache contains the following info
/// -1D : bc,e, xc,xw (bin content, error, x of center of bin, x bin width of bin)
/// -2D : bc,e, xc,xw,yc,yw
/// -3D : bc,e, xc,xw,yc,yw,zc,zw
virtual void FixParameter(Int_t ipar);/// Fix parameter ipar.
virtual void GetConfidenceIntervals(Int_t n, Int_t ndim, const Double_t *x, Double_t *ci, Double_t cl=0.95);
///Computes point-by-point confidence intervals for the fitted function
///Parameters:
///n - number of points
///ndim - dimensions of points
///x - points, at which to compute the intervals, for ndim > 1
/// should be in order: (x0,y0, x1, y1, ... xn, yn)
///ci - computed intervals are returned in this array
///cl - confidence level, default=0.95
///NOTE, that the intervals are approximate for nonlinear(in parameters) models
virtual void GetConfidenceIntervals(TObject *obj, Double_t cl=0.95);
///Computes confidence intervals at level cl. Default is 0.95
///The TObject parameter can be a TGraphErrors, a TGraph2DErrors or a TH1,2,3.
///For Graphs, confidence intervals are computed for each point,
///the value of the graph at that point is set to the function value at that
///point, and the graph y-errors (or z-errors) are set to the value of
///the confidence interval at that point.
///For Histograms, confidence intervals are computed for each bin center
///The bin content of this bin is then set to the function value at the bin
///center, and the bin error is set to the confidence interval value.
///NOTE: confidence intervals are approximate for nonlinear models!
///Allowed combinations:
///Fitted object Passed object
///TGraph TGraphErrors, TH1
///TGraphErrors, AsymmErrors TGraphErrors, TH1
///TH1 TGraphErrors, TH1
///TGraph2D TGraph2DErrors, TH2
///TGraph2DErrors TGraph2DErrors, TH2
///TH2 TGraph2DErrors, TH2
///TH3 TH3
virtual Double_t *GetCovarianceMatrix() const;/// return a pointer to the covariance matrix
virtual Double_t GetCovarianceMatrixElement(Int_t i, Int_t j) const;
/// return element i,j from the covariance matrix
virtual Int_t GetErrors(Int_t ipar,Double_t &eplus, Double_t &eminus, Double_t &eparab, Double_t &globcc) const;
/// return current errors for a parameter
/// ipar : parameter number
/// eplus : upper error
/// eminus : lower error
/// eparab : parabolic error
/// globcc : global correlation coefficient
TMinuit *GetMinuit() const {return fMinuit;}
virtual Int_t GetNumberTotalParameters() const;/// return the total number of parameters (free + fixed)
virtual Int_t GetNumberFreeParameters() const;/// return the number of free parameters
virtual Double_t GetParError(Int_t ipar) const;/// return error of parameter ipar
virtual Double_t GetParameter(Int_t ipar) const;
/// return current value of parameter ipar
virtual Int_t GetParameter(Int_t ipar,char *name,Double_t &value,Double_t &verr,Double_t &vlow, Double_t &vhigh) const;
/// return current values for a parameter
/// ipar : parameter number
/// parname : parameter name
/// value : initial parameter value
/// verr : initial error for this parameter
/// vlow : lower value for the parameter
/// vhigh : upper value for the parameter
/// WARNING! parname must be suitably dimensionned in the calling function.
virtual const char *GetParName(Int_t ipar) const;/// return name of parameter ipar
virtual Int_t GetStats(Double_t &amin, Double_t &edm, Double_t &errdef, Int_t &nvpar, Int_t &nparx) const;
/// return global fit parameters
/// amin : chisquare
/// edm : estimated distance to minimum
/// errdef
/// nvpar : number of variable parameters
/// nparx : total number of parameters
virtual Double_t GetSumLog(Int_t i);
/// return Sum(log(i) i=0,n
/// used by log likelihood fits
virtual Bool_t IsFixed(Int_t ipar) const;///return kTRUE if parameter ipar is fixed, kFALSE othersise)
virtual void PrintResults(Int_t level, Double_t amin) const;/// Print fit results
virtual void ReleaseParameter(Int_t ipar);/// Release parameter ipar.
virtual void SetFCN(void *fcn);
/// Specify the address of the fitting algorithm (from the interpreter)
virtual void SetFCN(void (*fcn)(Int_t &, Double_t *, Double_t &f, Double_t *, Int_t));
/// Specify the address of the fitting algorithm
virtual void SetFitMethod(const char *name);/// ret fit method (chisquare or loglikelihood)
virtual Int_t SetParameter(Int_t ipar,const char *parname,Double_t value,Double_t verr,Double_t vlow, Double_t vhigh);
/// set initial values for a parameter
/// ipar : parameter number
/// parname : parameter name
/// value : initial parameter value
/// verr : initial error for this parameter
/// vlow : lower value for the parameter
/// vhigh : upper value for the parameter