LSResidualFunc class description.
Internal class used for accessing the residuals of the Least Square function
and their derivates which are estimated numerically using GSL numerical derivation.
The class contains a pointer to the fit method function and an index specifying
the i-th residual and wraps it in a multi-dim gradient function interface
ROOT::Math::IGradientFunctionMultiDim.
The class is used by ROOT::Math::GSLNLSMinimizer (GSL non linear least square fitter)
@ingroup MultiMin
| virtual | ~GSLNLSMinimizer() |
| virtual void | ROOT::Math::Minimizer::Clear() |
| virtual double | CovMatrix(unsigned int, unsigned int) const |
| virtual double | Edm() const |
| virtual const double* | Errors() const |
| double | ROOT::Math::Minimizer::ErrorUp() const |
| virtual bool | GetMinosError(unsigned int, double&, double&) |
| ROOT::Math::GSLNLSMinimizer | GSLNLSMinimizer(int type = 0) |
| bool | ROOT::Math::Minimizer::IsValidError() const |
| unsigned int | ROOT::Math::Minimizer::MaxFunctionCalls() |
| unsigned int | ROOT::Math::Minimizer::MaxIterations() |
| virtual const double* | MinGradient() const |
| virtual bool | Minimize() |
| virtual double | MinValue() const |
| virtual unsigned int | NCalls() const |
| virtual unsigned int | NDim() const |
| virtual unsigned int | NFree() const |
| int | ROOT::Math::Minimizer::PrintLevel() const |
| virtual bool | ProvidesError() const |
| void | ROOT::Math::Minimizer::SetErrorUp(double up) |
| virtual bool | ROOT::Math::Minimizer::SetFixedVariable(unsigned int ivar, const string& name, double val) |
| virtual void | SetFunction(const ROOT::Math::IMultiGenFunction& func) |
| virtual void | SetFunction(const ROOT::Math::IMultiGradFunction& func) |
| virtual bool | ROOT::Math::Minimizer::SetLimitedVariable(unsigned int ivar, const string& name, double val, double step, double, double) |
| virtual bool | ROOT::Math::Minimizer::SetLowerLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower) |
| void | ROOT::Math::Minimizer::SetMaxFunctionCalls(unsigned int maxfcn) |
| void | ROOT::Math::Minimizer::SetMaxIterations(unsigned int maxiter) |
| void | ROOT::Math::Minimizer::SetPrintLevel(int level) |
| void | ROOT::Math::Minimizer::SetStrategy(int strategyLevel) |
| void | ROOT::Math::Minimizer::SetTolerance(double tol) |
| virtual bool | ROOT::Math::Minimizer::SetUpperLimitedVariable(unsigned int ivar, const string& name, double val, double step, double upper) |
| void | ROOT::Math::Minimizer::SetValidError(bool on) |
| virtual bool | SetVariable(unsigned int ivar, const string& name, double val, double step) |
| int | ROOT::Math::Minimizer::Strategy() const |
| double | ROOT::Math::Minimizer::Tolerance() const |
| virtual const double* | X() const |
| ROOT::Math::GSLNLSMinimizer | GSLNLSMinimizer(const ROOT::Math::GSLNLSMinimizer&) |
| ROOT::Math::GSLNLSMinimizer& | operator=(const ROOT::Math::GSLNLSMinimizer& rhs) |
| int | ROOT::Math::Minimizer::fDebug | print level |
| unsigned int | ROOT::Math::Minimizer::fMaxCalls | max number of funciton calls |
| unsigned int | ROOT::Math::Minimizer::fMaxIter | max number or iterations used to find the minimum |
| int | ROOT::Math::Minimizer::fStrategy | minimizer strategy |
| double | ROOT::Math::Minimizer::fTol | tolerance (absolute) |
| double | ROOT::Math::Minimizer::fUp | error scale |
| bool | ROOT::Math::Minimizer::fValidError | flag to control if errors have been validated (Hesse has been run in case of Minuit) |
| unsigned int | fDim | |
| ROOT::Math::GSLMultiFit* | fGSLMultiFit | pointer to GSL multi fit solver |
| double | fLSTolerance | Line Search Tolerance |
| double | fMinVal | minimum function value |
| vector<std::string> | fNames | |
| const ROOT::Math::IMultiGenFunction* | fObjFunc | pointer to Least square function |
| vector<LSResidualFunc> | fResiduals | ! transient Vector of the residual functions |
| unsigned int | fSize | |
| vector<double> | fSteps | |
| vector<double> | fValues |
usually copying is non trivial, so we make this unaccessible
Copy constructor
{}number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total
{ return fDim; } return covariance matrices elements
if the variable is fixed the matrix is zero
The ordering of the variables is the same as in errors
{ return 0; }minos error for variable i, return false if Minos failed
{ return false; }