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ROOT
Reference Guide |
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ROOT
Reference Guide |
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Interface (abstract class) defining the function to be minimized, which has to be implemented by the user.
Public Member Functions | |
| virtual | ~FCNBase ()=default |
| virtual double | ErrorDef () const |
| Error definition of the function. | |
| virtual std::vector< double > | G2 (std::vector< double > const &) const |
| Return the diagonal elements of the Hessian (second derivatives). | |
| virtual std::vector< double > | Gradient (std::vector< double > const &) const |
| Return the gradient vector of the function at the given parameter point. | |
| virtual std::vector< double > | GradientWithPrevResult (std::vector< double > const ¶meters, double *, double *, double *) const |
| virtual GradientParameterSpace | gradParameterSpace () const |
| virtual bool | HasG2 () const |
| virtual bool | HasGradient () const |
| virtual bool | HasHessian () const |
| virtual std::vector< double > | Hessian (std::vector< double > const &) const |
| Return the full Hessian matrix of the function. | |
| virtual double | operator() (std::vector< double > const &v) const =0 |
| The meaning of the vector of parameters is of course defined by the user, who uses the values of those parameters to calculate their function Value. | |
| virtual void | SetErrorDef (double) |
| add interface to set dynamically a new error definition Re-implement this function if needed. | |
| virtual double | Up () const =0 |
| Error definition of the function. | |
#include <Minuit2/FCNBase.h>
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virtualdefault |
Error definition of the function.
MINUIT defines Parameter errors as the change in Parameter Value required to change the function Value by up. Normally, for chisquared fits it is 1, and for negative log likelihood, its Value is 0.5. If the user wants instead the 2-sigma errors for chisquared fits, it becomes 4, as Chi2(x+n*sigma) = Chi2(x) + n*n.
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inlinevirtual |
Return the diagonal elements of the Hessian (second derivatives).
By default, returns an empty vector. Override this method if analytic second derivatives (per-parameter curvature) are available.
| v | Parameter vector. |
Reimplemented in ROOT::Minuit2::FCNAdapter.
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inlinevirtual |
Return the gradient vector of the function at the given parameter point.
By default, returns an empty vector (no analytic gradient provided). Override this method if an analytic gradient is available.
| v | Parameter vector. |
Reimplemented in ROOT::Minuit2::FumiliFCNBase, and ROOT::Minuit2::FCNAdapter.
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inlinevirtual |
Reimplemented in ROOT::Minuit2::FCNAdapter.
Reimplemented in ROOT::Minuit2::FCNGradientBase, ROOT::Minuit2::FCNAdapter, and ROOT::Minuit2::FumiliFCNBase.
Reimplemented in ROOT::Minuit2::FCNAdapter.
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inlinevirtual |
Return the full Hessian matrix of the function.
By default, returns an empty vector. Override this method if the full analytic Hessian (matrix of second derivatives) is available.
| v | Parameter vector. |
Reimplemented in ROOT::Minuit2::FumiliFCNBase, and ROOT::Minuit2::FCNAdapter.
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pure virtual |
The meaning of the vector of parameters is of course defined by the user, who uses the values of those parameters to calculate their function Value.
The order and the position of these parameters is strictly the one specified by the user when supplying the starting values for minimization. The starting values must be specified by the user, either via an std::vector<double> or the MnUserParameters supplied as input to the MINUIT minimizers such as VariableMetricMinimizer or MnMigrad. Later values are determined by MINUIT as it searches for the Minimum or performs whatever analysis is requested by the user.
| v | function parameters as defined by the user. |
Implemented in ROOT::Minuit2::FumiliChi2FCN, ROOT::Minuit2::FumiliMaximumLikelihoodFCN, ROOT::Minuit2::FCNAdapter, ROOT::Minuit2::FumiliFCNAdapter< Function >, and ROOT::Minuit2::ParametricFunction.
add interface to set dynamically a new error definition Re-implement this function if needed.
Reimplemented in ROOT::Minuit2::FCNAdapter, and ROOT::Minuit2::FumiliFCNAdapter< Function >.
Error definition of the function.
MINUIT defines Parameter errors as the change in Parameter Value required to change the function Value by up. Normally, for chisquared fits it is 1, and for negative log likelihood, its Value is 0.5. If the user wants instead the 2-sigma errors for chisquared fits, it becomes 4, as Chi2(x+n*sigma) = Chi2(x) + n*n.
Implemented in ROOT::Minuit2::FCNAdapter, ROOT::Minuit2::FumiliChi2FCN, ROOT::Minuit2::FumiliFCNAdapter< Function >, and ROOT::Minuit2::FumiliMaximumLikelihoodFCN.
