#include <fitting.h>

 FunctionFitDerivative () 

 ~FunctionFitDerivative () 

bool  init (ModelFunction &model_func, unsigned int nvals) 

bool  fit (const Array< float, 1 > &yvals, const Array< float, 1 > &ysigma=defaultArray, const Array< float, 1 > &xvals=defaultArray, unsigned int max_iterations=DEFAULT_MAX_ITER, double tolerance=DEFAULT_TOLERANCE) 

Class which is used for derivativebased fitting of functions.
Definition at line 159 of file fitting.h.
§ FunctionFitDerivative()
FunctionFitDerivative::FunctionFitDerivative 
( 
 ) 


inline 
Constructs uninitialized function fit
Definition at line 166 of file fitting.h.
§ ~FunctionFitDerivative()
FunctionFitDerivative::~FunctionFitDerivative 
( 
 ) 

§ fit()
bool FunctionFitDerivative::fit 
( 
const Array< float, 1 > & 
yvals, 


const Array< float, 1 > & 
ysigma = defaultArray , 


const Array< float, 1 > & 
xvals = defaultArray , 


unsigned int 
max_iterations = DEFAULT_MAX_ITER , 


double 
tolerance = DEFAULT_TOLERANCE 

) 
 

virtual 
The fitting routine that takes the starting values from the model function, yvalues 'yvals', and optionally the corresponding yerror bars 'ysigma' and xvals 'xvals'. If no errorbars are given, they are all set to 0.1 and if no xvals are given equidistant points with an increment of one are chosen, i.e. xvals(i)=i; A maximum of 'max_iterations' iterations and the given 'tolerance' is used during the fit. Returns true on success.
Implements FunctionFitInterface.
§ init()
bool FunctionFitDerivative::init 
( 
ModelFunction & 
model_func, 


unsigned int 
nvals 

) 
 

virtual 
Prepare a nonlinear leastsquare fit of function 'model_func' for 'nvals' values
Implements FunctionFitInterface.
The documentation for this class was generated from the following file: