Logo ROOT   6.19/01
Reference Guide
Fitter.h
Go to the documentation of this file.
1 // @(#)root/mathcore:$Id$
2 // Author: L. Moneta Wed Aug 30 11:05:19 2006
3 
4 /**********************************************************************
5  * *
6  * Copyright (c) 2006 LCG ROOT Math Team, CERN/PH-SFT *
7  * *
8  * *
9  **********************************************************************/
10 
11 // Header file for class Fitter
12 
13 #ifndef ROOT_Fit_Fitter
14 #define ROOT_Fit_Fitter
15 
16 /**
17 @defgroup Fit Fitting and Parameter Estimation
18 
19 Classes used for fitting (regression analysis) and estimation of parameter values given a data sample.
20 
21 @ingroup MathCore
22 
23 */
24 
25 #include "Fit/BinData.h"
26 #include "Fit/UnBinData.h"
27 #include "Fit/FitConfig.h"
28 #include "Fit/FitExecutionPolicy.h"
29 #include "Fit/FitResult.h"
30 #include "Math/IParamFunction.h"
31 #include <memory>
32 
33 namespace ROOT {
34 
35 
36  namespace Math {
37  class Minimizer;
38 
39  // should maybe put this in a FitMethodFunctionfwd file
40  template<class FunctionType> class BasicFitMethodFunction;
41 
42  // define the normal and gradient function
45 
46  }
47 
48  /**
49  Namespace for the fitting classes
50  @ingroup Fit
51  */
52 
53  namespace Fit {
54 
55 /**
56  @defgroup FitMain User Fitting classes
57 
58  Main Classes used for fitting a given data set
59  @ingroup Fit
60 */
61 
62 
63 //___________________________________________________________________________________
64 /**
65  Fitter class, entry point for performing all type of fits.
66  Fits are performed using the generic ROOT::Fit::Fitter::Fit method.
67  The inputs are the data points and a model function (using a ROOT::Math::IParamFunction)
68  The result of the fit is returned and kept internally in the ROOT::Fit::FitResult class.
69  The configuration of the fit (parameters, options, etc...) are specified in the
70  ROOT::Math::FitConfig class.
71  After fitting the config of the fit will be modified to have the new values the resulting
72  parameter of the fit with step sizes equal to the errors. FitConfig can be preserved with
73  initial parameters by calling FitConfig.SetUpdateAfterFit(false);
74 
75  @ingroup FitMain
76 */
77 class Fitter {
78 
79 public:
80 
82  template <class T>
84 #ifdef R__HAS_VECCORE
87 #else
90 #endif
94 
97 
98 
99  /**
100  Default constructor
101  */
102  Fitter ();
103 
104  /**
105  Constructor from a result
106  */
107  Fitter (const std::shared_ptr<FitResult> & result);
108 
109 
110  /**
111  Destructor
112  */
113  ~Fitter ();
114 
115 private:
116 
117  /**
118  Copy constructor (disabled, class is not copyable)
119  */
120  Fitter(const Fitter &);
121 
122  /**
123  Assignment operator (disabled, class is not copyable)
124  */
125  Fitter & operator = (const Fitter & rhs);
126 
127 
128 public:
129 
130  /**
131  fit a data set using any generic model function
132  If data set is binned a least square fit is performed
133  If data set is unbinned a maximum likelihood fit (not extended) is done
134  Pre-requisite on the function:
135  it must implement the 1D or multidimensional parametric function interface
136  */
137  template <class Data, class Function,
138  class cond = typename std::enable_if<!(std::is_same<Function, ROOT::Fit::ExecutionPolicy>::value ||
139  std::is_same<Function, int>::value),
140  Function>::type>
141  bool Fit(const Data &data, const Function &func,
143  {
144  SetFunction(func);
145  return Fit(data, executionPolicy);
146  }
147 
148  /**
149  Fit a binned data set using a least square fit (default method)
150  */
151  bool Fit(const BinData & data, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) {
152  SetData(data);
153  return DoLeastSquareFit(executionPolicy);
154  }
155  bool Fit(const std::shared_ptr<BinData> & data, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) {
156  SetData(data);
157  return DoLeastSquareFit(executionPolicy);
158  }
159 
160  /**
161  Fit a binned data set using a least square fit
162  */
163  bool LeastSquareFit(const BinData & data) {
164  return Fit(data);
165  }
166 
167  /**
168  fit an unbinned data set using loglikelihood method
169  */
170  bool Fit(const UnBinData & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) {
171  SetData(data);
172  return DoUnbinnedLikelihoodFit(extended, executionPolicy);
173  }
174 
175  /**
176  Binned Likelihood fit. Default is extended
177  */
178  bool LikelihoodFit(const BinData &data, bool extended = true,
180  SetData(data);
181  return DoBinnedLikelihoodFit(extended, executionPolicy);
182  }
183 
184  bool LikelihoodFit(const std::shared_ptr<BinData> &data, bool extended = true,
186  SetData(data);
187  return DoBinnedLikelihoodFit(extended, executionPolicy);
188  }
189  /**
190  Unbinned Likelihood fit. Default is not extended
191  */
192  bool LikelihoodFit(const UnBinData & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) {
193  SetData(data);
194  return DoUnbinnedLikelihoodFit(extended, executionPolicy);
195  }
196  bool LikelihoodFit(const std::shared_ptr<UnBinData> & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) {
197  SetData(data);
198  return DoUnbinnedLikelihoodFit(extended, executionPolicy);
199  }
200 
201 
202  /**
203  fit a data set using any generic model function
204  Pre-requisite on the function:
205  */
206  template < class Data , class Function>
207  bool LikelihoodFit( const Data & data, const Function & func, bool extended) {
208  SetFunction(func);
209  return LikelihoodFit(data, extended);
210  }
211 
212  /**
213  do a linear fit on a set of bin-data
214  */
215  bool LinearFit(const BinData & data) {
216  SetData(data);
217  return DoLinearFit();
218  }
219  bool LinearFit(const std::shared_ptr<BinData> & data) {
220  SetData(data);
221  return DoLinearFit();
222  }
223 
224 
225  /**
226  Fit using the a generic FCN function as a C++ callable object implementing
227  double () (const double *)
228  Note that the function dimension (i.e. the number of parameter) is needed in this case
229  For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..)
230  */
231  template <class Function>
232  bool FitFCN(unsigned int npar, Function & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
233 
234  /**
235  Set a generic FCN function as a C++ callable object implementing
236  double () (const double *)
237  Note that the function dimension (i.e. the number of parameter) is needed in this case
238  For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..)
239  */
240  template <class Function>
241  bool SetFCN(unsigned int npar, Function & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
242 
243  /**
244  Fit using the given FCN function represented by a multi-dimensional function interface
245  (ROOT::Math::IMultiGenFunction).
246  Give optionally the initial arameter values, data size to have the fit Ndf correctly
247  set in the FitResult and flag specifying if it is a chi2 fit.
248  Note that if the parameters values are not given (params=0) the
249  current parameter settings are used. The parameter settings can be created before
250  by using the FitConfig::SetParamsSetting. If they have not been created they are created
251  automatically when the params pointer is not zero.
252  Note that passing a params != 0 will set the parameter settings to the new value AND also the
253  step sizes to some pre-defined value (stepsize = 0.3 * abs(parameter_value) )
254  */
255  bool FitFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool
256  chi2fit = false);
257 
258  /**
259  Fit using a FitMethodFunction interface. Same as method above, but now extra information
260  can be taken from the function class
261  */
262  bool FitFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0);
263 
264  /**
265  Set the FCN function represented by a multi-dimensional function interface
266  (ROOT::Math::IMultiGenFunction) and optionally the initial parameters
267  See also note above for the initial parameters for FitFCN
268  */
269  bool SetFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
270 
271  /**
272  Set the objective function (FCN) using a FitMethodFunction interface.
273  Same as method above, but now extra information can be taken from the function class
274  */
275  bool SetFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0);
276 
277  /**
278  Fit using the given FCN function representing a multi-dimensional gradient function
279  interface (ROOT::Math::IMultiGradFunction). In this case the minimizer will use the
280  gradient information provided by the function.
281  For the options same consideration as in the previous method
282  */
283  bool FitFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
284 
285  /**
286  Fit using a FitMethodGradFunction interface. Same as method above, but now extra information
287  can be taken from the function class
288  */
289  bool FitFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0);
290 
291  /**
292  Set the FCN function represented by a multi-dimensional gradient function interface
293  (ROOT::Math::IMultiGenFunction) and optionally the initial parameters
294  See also note above for the initial parameters for FitFCN
295  */
296  bool SetFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
297 
298  /**
299  Set the objective function (FCN) using a FitMethodGradFunction interface.
300  Same as method above, but now extra information can be taken from the function class
301  */
302  bool SetFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0);
303 
304 
305  /**
306  fit using user provided FCN with Minuit-like interface
307  If npar = 0 it is assumed that the parameters are specified in the parameter settings created before
308  For the options same consideration as in the previous method
309  */
310  typedef void (* MinuitFCN_t )(int &npar, double *gin, double &f, double *u, int flag);
311  bool FitFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
312 
313  /**
314  set objective function using user provided FCN with Minuit-like interface
315  If npar = 0 it is assumed that the parameters are specified in the parameter settings created before
316  For the options same consideration as in the previous method
317  */
318  bool SetFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
319 
320  /**
321  Perform a fit with the previously set FCN function. Require SetFCN before
322  */
323  bool FitFCN();
324 
325  /**
326  Perform a simple FCN evaluation. FitResult will be modified and contain the value of the FCN
327  */
328  bool EvalFCN();
329 
330 
331 
332  /**
333  Set the fitted function (model function) from a parametric function interface
334  */
335  void SetFunction(const IModelFunction & func, bool useGradient = false);
336 
337  /**
338  Set the fitted function (model function) from a vectorized parametric function interface
339  */
340 #ifdef R__HAS_VECCORE
341  template <class NotCompileIfScalarBackend = std::enable_if<!(std::is_same<double, ROOT::Double_v>::value)>>
342  void SetFunction(const IModelFunction_v &func, bool useGradient = false);
343 
344  template <class NotCompileIfScalarBackend = std::enable_if<!(std::is_same<double, ROOT::Double_v>::value)>>
345  void SetFunction(const IGradModelFunction_v &func, bool useGradient = true);
346 #endif
347  /**
348  Set the fitted function from a parametric 1D function interface
349  */
350  void SetFunction(const IModel1DFunction & func, bool useGradient = false);
351 
352  /**
353  Set the fitted function (model function) from a parametric gradient function interface
354  */
355  void SetFunction(const IGradModelFunction & func, bool useGradient = true);
356  /**
357  Set the fitted function from 1D gradient parametric function interface
358  */
359  void SetFunction(const IGradModel1DFunction & func, bool useGradient = true);
360 
361 
362  /**
363  get fit result
364  */
365  const FitResult & Result() const {
366  assert( fResult.get() );
367  return *fResult;
368  }
369 
370 
371  /**
372  perform an error analysis on the result using the Hessian
373  Errors are obtaied from the inverse of the Hessian matrix
374  To be called only after fitting and when a minimizer supporting the Hessian calculations is used
375  otherwise an error (false) is returned.
376  A new FitResult with the Hessian result will be produced
377  */
378  bool CalculateHessErrors();
379 
380  /**
381  perform an error analysis on the result using MINOS
382  To be called only after fitting and when a minimizer supporting MINOS is used
383  otherwise an error (false) is returned.
384  The result will be appended in the fit result class
385  Optionally a vector of parameter indeces can be passed for selecting
386  the parameters to analyse using FitConfig::SetMinosErrors
387  */
388  bool CalculateMinosErrors();
389 
390  /**
391  access to the fit configuration (const method)
392  */
393  const FitConfig & Config() const { return fConfig; }
394 
395  /**
396  access to the configuration (non const method)
397  */
398  FitConfig & Config() { return fConfig; }
399 
400  /**
401  query if fit is binned. In cse of false teh fit can be unbinned
402  or is not defined (like in case of fitting through a ::FitFCN)
403  */
404  bool IsBinFit() const { return fBinFit; }
405 
406  /**
407  return pointer to last used minimizer
408  (is NULL in case fit is not yet done)
409  This pointer is guranteed to be valid as far as the fitter class is valid and a new fit is not redone.
410  To be used only after fitting.
411  The pointer should not be stored and will be invalided after performing a new fitting.
412  In this case a new instance of ROOT::Math::Minimizer will be re-created and can be
413  obtained calling again GetMinimizer()
414  */
415  ROOT::Math::Minimizer * GetMinimizer() const { return fMinimizer.get(); }
416 
417  /**
418  return pointer to last used objective function
419  (is NULL in case fit is not yet done)
420  This pointer will be valid as far as the fitter class
421  has not been deleted. To be used after the fitting.
422  The pointer should not be stored and will be invalided after performing a new fitting.
423  In this case a new instance of the function pointer will be re-created and can be
424  obtained calling again GetFCN()
425  */
427 
428 
429  /**
430  apply correction in the error matrix for the weights for likelihood fits
431  This method can be called only after a fit. The
432  passed function (loglw2) is a log-likelihood function impelemented using the
433  sum of weight squared
434  When using FitConfig.SetWeightCorrection() this correction is applied
435  automatically when doing a likelihood fit (binned or unbinned)
436  */
437  bool ApplyWeightCorrection(const ROOT::Math::IMultiGenFunction & loglw2, bool minimizeW2L=false);
438 
439 
440 protected:
441 
442 
443  /// least square fit
445  /// binned likelihood fit
446  bool DoBinnedLikelihoodFit(bool extended = true, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial);
447  /// un-binned likelihood fit
448  bool DoUnbinnedLikelihoodFit( bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial);
449  /// linear least square fit
450  bool DoLinearFit();
451 
452  // initialize the minimizer
453  bool DoInitMinimizer();
454  /// do minimization
455  bool DoMinimization(const BaseFunc & f, const ROOT::Math::IMultiGenFunction * chifunc = 0);
456  // do minimization after having set obj function
457  bool DoMinimization(const ROOT::Math::IMultiGenFunction * chifunc = 0);
458  // update config after fit
459  void DoUpdateFitConfig();
460  // get function calls from the FCN
461  int GetNCallsFromFCN();
462 
463 
464  //set data for the fit
465  void SetData(const FitData & data) {
466  fData = std::shared_ptr<FitData>(const_cast<FitData*>(&data),DummyDeleter<FitData>());
467  }
468  // set data and function without cloning them
469  template <class T>
470  void SetFunctionAndData(const IModelFunctionTempl<T> & func, const FitData & data) {
471  SetData(data);
472  fFunc = std::shared_ptr<IModelFunctionTempl<T>>(const_cast<IModelFunctionTempl<T>*>(&func),DummyDeleter<IModelFunctionTempl<T>>());
473  }
474 
475  //set data for the fit using a shared ptr
476  template <class Data>
477  void SetData(const std::shared_ptr<Data> & data) {
478  fData = std::static_pointer_cast<Data>(data);
479  }
480 
481  /// look at the user provided FCN and get data and model function is
482  /// they derive from ROOT::Fit FCN classes
483  void ExamineFCN();
484 
485 
486  /// internal functions to get data set and model function from FCN
487  /// useful for fits done with customized FCN classes
488  template <class ObjFuncType>
489  bool GetDataFromFCN();
490 
491 
492 private:
493 
494  bool fUseGradient; // flag to indicate if using gradient or not
495 
496  bool fBinFit; // flag to indicate if fit is binned
497  // in case of false the fit is unbinned or undefined)
498  // flag it is used to compute chi2 for binned likelihood fit
499 
500  int fFitType; // type of fit (0 undefined, 1 least square, 2 likelihood)
501 
502  int fDataSize; // size of data sets (need for Fumili or LM fitters)
503 
504  FitConfig fConfig; // fitter configuration (options and parameter settings)
505 
506  std::shared_ptr<IModelFunction_v> fFunc_v; //! copy of the fitted function containing on output the fit result
507 
508  std::shared_ptr<IModelFunction> fFunc; //! copy of the fitted function containing on output the fit result
509 
510  std::shared_ptr<ROOT::Fit::FitResult> fResult; //! pointer to the object containing the result of the fit
511 
512  std::shared_ptr<ROOT::Math::Minimizer> fMinimizer; //! pointer to used minimizer
513 
514  std::shared_ptr<ROOT::Fit::FitData> fData; //! pointer to the fit data (binned or unbinned data)
515 
516  std::shared_ptr<ROOT::Math::IMultiGenFunction> fObjFunction; //! pointer to used objective function
517 
518 };
519 
520 
521 // internal functions to get data set and model function from FCN
522 // useful for fits done with customized FCN classes
523 template <class ObjFuncType>
525  ObjFuncType * objfunc = dynamic_cast<ObjFuncType*>(fObjFunction.get() );
526  if (objfunc) {
527  fFunc = objfunc->ModelFunctionPtr();
528  fData = objfunc->DataPtr();
529  return true;
530  }
531  else {
532  return false;
533  }
534 }
535 
536 #ifdef R__HAS_VECCORE
537 template <class NotCompileIfScalarBackend>
538 void Fitter::SetFunction(const IModelFunction_v &func, bool useGradient)
539 {
540  fUseGradient = useGradient;
541  if (fUseGradient) {
542  const IGradModelFunction_v *gradFunc = dynamic_cast<const IGradModelFunction_v *>(&func);
543  if (gradFunc) {
544  SetFunction(*gradFunc, true);
545  return;
546  } else {
547  MATH_WARN_MSG("Fitter::SetFunction",
548  "Requested function does not provide gradient - use it as non-gradient function ");
549  }
550  }
551 
552  // set the fit model function (clone the given one and keep a copy )
553  // std::cout << "set a non-grad function" << std::endl;
554  fUseGradient = false;
555  fFunc_v = std::shared_ptr<IModelFunction_v>(dynamic_cast<IModelFunction_v *>(func.Clone()));
556  assert(fFunc_v);
557 
558  // creates the parameter settings
560  fFunc.reset();
561 }
562 
563 template <class NotCompileIfScalarBackend>
564 void Fitter::SetFunction(const IGradModelFunction_v &func, bool useGradient)
565 {
566  fUseGradient = useGradient;
567 
568  // set the fit model function (clone the given one and keep a copy )
569  fFunc_v = std::shared_ptr<IModelFunction_v>(dynamic_cast<IGradModelFunction_v *>(func.Clone()));
570  assert(fFunc_v);
571 
572  // creates the parameter settings
574  fFunc.reset();
575 }
576 #endif
577 
578  } // end namespace Fit
579 
580 } // end namespace ROOT
581 
582 // implementation of inline methods
583 
584 
585 #ifndef __CINT__
586 
587 #include "Math/WrappedFunction.h"
588 
589 template<class Function>
590 bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) {
592  return FitFCN(wf,par,datasize,chi2fit);
593 }
594 template<class Function>
595 bool ROOT::Fit::Fitter::SetFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) {
597  return SetFCN(wf,par,datasize,chi2fit);
598 }
599 
600 
601 
602 
603 #endif // endif __CINT__
604 
605 #endif /* ROOT_Fit_Fitter */
ROOT::Math::IParamFunction IModel1DFunction
Definition: Fitter.h:92
bool Fit(const UnBinData &data, bool extended=false, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
fit an unbinned data set using loglikelihood method
Definition: Fitter.h:170
void ExamineFCN()
look at the user provided FCN and get data and model function is they derive from ROOT::Fit FCN class...
Definition: Fitter.cxx:984
Interface (abstract class) for multi-dimensional functions providing a gradient calculation.
Definition: IFunction.h:326
VSD Structures.
Definition: StringConv.hxx:21
void(* MinuitFCN_t)(int &npar, double *gin, double &f, double *u, int flag)
fit using user provided FCN with Minuit-like interface If npar = 0 it is assumed that the parameters ...
Definition: Fitter.h:310
FitConfig & Config()
access to the configuration (non const method)
Definition: Fitter.h:398
bool CalculateMinosErrors()
perform an error analysis on the result using MINOS To be called only after fitting and when a minimi...
Definition: Fitter.cxx:696
bool Fit(const BinData &data, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Fit a binned data set using a least square fit (default method)
Definition: Fitter.h:151
Fitter & operator=(const Fitter &rhs)
Assignment operator (disabled, class is not copyable)
Definition: Fitter.cxx:84
bool EvalFCN()
Perform a simple FCN evaluation.
Definition: Fitter.cxx:311
Base class for all the fit data types: Stores the coordinates and the DataOptions.
Definition: FitData.h:66
void SetData(const FitData &data)
Definition: Fitter.h:465
bool LikelihoodFit(const UnBinData &data, bool extended=false, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Unbinned Likelihood fit.
Definition: Fitter.h:192
ROOT::Math::IMultiGenFunction BaseFunc
Definition: Fitter.h:95
std::shared_ptr< IModelFunction > fFunc
copy of the fitted function containing on output the fit result
Definition: Fitter.h:508
Class describing the unbinned data sets (just x coordinates values) of any dimensions.
Definition: UnBinData.h:42
#define f(i)
Definition: RSha256.hxx:104
Interface (abstract class) for parametric gradient multi-dimensional functions providing in addition ...
bool LikelihoodFit(const BinData &data, bool extended=true, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Binned Likelihood fit.
Definition: Fitter.h:178
bool DoLinearFit()
linear least square fit
Definition: Fitter.cxx:603
void SetFunctionAndData(const IModelFunctionTempl< T > &func, const FitData &data)
Definition: Fitter.h:470
#define MATH_WARN_MSG(loc, str)
Definition: Error.h:79
std::shared_ptr< ROOT::Fit::FitResult > fResult
copy of the fitted function containing on output the fit result
Definition: Fitter.h:510
bool CalculateHessErrors()
perform an error analysis on the result using the Hessian Errors are obtaied from the inverse of the ...
Definition: Fitter.cxx:620
bool LeastSquareFit(const BinData &data)
Fit a binned data set using a least square fit.
Definition: Fitter.h:163
bool DoBinnedLikelihoodFit(bool extended=true, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
binned likelihood fit
Definition: Fitter.cxx:389
const FitResult & Result() const
get fit result
Definition: Fitter.h:365
void CreateParamsSettings(const ROOT::Math::IParamMultiFunctionTempl< T > &func)
set the parameter settings from a model function.
Definition: FitConfig.h:108
Fitter()
Default constructor.
Definition: Fitter.cxx:51
bool DoInitMinimizer()
Definition: Fitter.cxx:761
ROOT::Math::IParamMultiGradFunction IGradModelFunction
Definition: Fitter.h:91
Abstract Minimizer class, defining the interface for the various minimizer (like Minuit2, Minuit, GSL, etc..) Plug-in&#39;s exist in ROOT to be able to instantiate the derived classes like ROOT::Math::GSLMinimizer or ROOT::Math::Minuit2Minimizer via the plug-in manager.
Definition: Minimizer.h:78
bool DoLeastSquareFit(const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
least square fit
Definition: Fitter.cxx:330
ROOT::Math::IParamGradFunction IGradModel1DFunction
Definition: Fitter.h:93
bool LinearFit(const BinData &data)
do a linear fit on a set of bin-data
Definition: Fitter.h:215
bool Fit(const std::shared_ptr< BinData > &data, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Definition: Fitter.h:155
std::shared_ptr< IModelFunction_v > fFunc_v
Definition: Fitter.h:506
bool ApplyWeightCorrection(const ROOT::Math::IMultiGenFunction &loglw2, bool minimizeW2L=false)
apply correction in the error matrix for the weights for likelihood fits This method can be called on...
Definition: Fitter.cxx:877
const FitConfig & Config() const
access to the fit configuration (const method)
Definition: Fitter.h:393
Documentation for the abstract class IBaseFunctionMultiDim.
Definition: IFunction.h:62
~Fitter()
Destructor.
Definition: Fitter.cxx:70
std::shared_ptr< ROOT::Fit::FitData > fData
pointer to used minimizer
Definition: Fitter.h:514
FitConfig fConfig
Definition: Fitter.h:504
Double_t(* Function)(Double_t)
Definition: Functor.C:4
bool IsBinFit() const
query if fit is binned.
Definition: Fitter.h:404
RooCmdArg Minimizer(const char *type, const char *alg=0)
Fitter class, entry point for performing all type of fits.
Definition: Fitter.h:77
ROOT::Math::IParamMultiGradFunction IGradModelFunction_v
Definition: Fitter.h:89
ROOT::Math::IMultiGenFunction * GetFCN() const
return pointer to last used objective function (is NULL in case fit is not yet done) This pointer wil...
Definition: Fitter.h:426
bool LikelihoodFit(const std::shared_ptr< UnBinData > &data, bool extended=false, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Definition: Fitter.h:196
bool FitFCN()
Perform a fit with the previously set FCN function.
Definition: Fitter.cxx:296
Class describing the binned data sets : vectors of x coordinates, y values and optionally error on y ...
Definition: BinData.h:53
void SetFunction(const IModelFunction &func, bool useGradient=false)
Set the fitted function (model function) from a parametric function interface.
Definition: Fitter.cxx:103
std::shared_ptr< ROOT::Math::Minimizer > fMinimizer
pointer to the object containing the result of the fit
Definition: Fitter.h:512
Interface (abstract class) for parametric one-dimensional gradient functions providing in addition to...
IParamFunction interface (abstract class) describing multi-dimensional parameteric functions It is a ...
class containg the result of the fit and all the related information (fitted parameter values...
Definition: FitResult.h:48
Specialized IParamFunction interface (abstract class) for one-dimensional parametric functions It is ...
FitMethodFunction class Interface for objective functions (like chi2 and likelihood used in the fit) ...
Definition: Fitter.h:40
TFitResultPtr Fit(FitObject *h1, TF1 *f1, Foption_t &option, const ROOT::Math::MinimizerOptions &moption, const char *goption, ROOT::Fit::DataRange &range)
Definition: HFitImpl.cxx:134
bool DoUnbinnedLikelihoodFit(bool extended=false, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
un-binned likelihood fit
Definition: Fitter.cxx:498
int type
Definition: TGX11.cxx:120
ROOT::Math::IMultiGradFunction BaseGradFunc
Definition: Fitter.h:96
std::shared_ptr< ROOT::Math::IMultiGenFunction > fObjFunction
pointer to the fit data (binned or unbinned data)
Definition: Fitter.h:516
bool GetDataFromFCN()
internal functions to get data set and model function from FCN useful for fits done with customized F...
Definition: Fitter.h:524
Template class to wrap any C++ callable object implementing operator() (const double * x) in a multi-...
Namespace for new Math classes and functions.
bool fUseGradient
Definition: Fitter.h:494
BasicFitMethodFunction< ROOT::Math::IMultiGradFunction > FitMethodGradFunction
Definition: Fitter.h:44
typedef void((*Func_t)())
bool SetFCN(unsigned int npar, Function &fcn, const double *params=0, unsigned int dataSize=0, bool chi2fit=false)
Set a generic FCN function as a C++ callable object implementing double () (const double *) Note that...
Definition: Fitter.h:595
bool LikelihoodFit(const std::shared_ptr< BinData > &data, bool extended=true, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
Definition: Fitter.h:184
ROOT::Math::IParamMultiFunction IModelFunction_v
Definition: Fitter.h:88
int GetNCallsFromFCN()
Definition: Fitter.cxx:861
ROOT::Math::Minimizer * GetMinimizer() const
return pointer to last used minimizer (is NULL in case fit is not yet done) This pointer is guranteed...
Definition: Fitter.h:415
void DoUpdateFitConfig()
Definition: Fitter.cxx:851
bool LikelihoodFit(const Data &data, const Function &func, bool extended)
fit a data set using any generic model function Pre-requisite on the function:
Definition: Fitter.h:207
bool Fit(const Data &data, const Function &func, const ROOT::Fit::ExecutionPolicy &executionPolicy=ROOT::Fit::ExecutionPolicy::kSerial)
fit a data set using any generic model function If data set is binned a least square fit is performed...
Definition: Fitter.h:141
bool LinearFit(const std::shared_ptr< BinData > &data)
Definition: Fitter.h:219
void SetData(const std::shared_ptr< Data > &data)
Definition: Fitter.h:477
bool DoMinimization(const BaseFunc &f, const ROOT::Math::IMultiGenFunction *chifunc=0)
do minimization
Definition: Fitter.cxx:840
ROOT::Math::IParamMultiFunction IModelFunction
Definition: Fitter.h:81
Class describing the configuration of the fit, options and parameter settings using the ROOT::Fit::Pa...
Definition: FitConfig.h:46
BasicFitMethodFunction< ROOT::Math::IMultiGenFunction > FitMethodFunction
Definition: Fitter.h:40