// @(#)root/mathcore:$Id$
// Author: L. Moneta Wed Aug 30 11:05:19 2006

/**********************************************************************
 *                                                                    *
 * Copyright (c) 2006  LCG ROOT Math Team, CERN/PH-SFT                *
 *                                                                    *
 *                                                                    *
 **********************************************************************/

// Header file for class Fitter

#ifndef ROOT_Fit_Fitter
#define ROOT_Fit_Fitter

/**
@defgroup Fit Fitting and Parameter Estimation

Classes used for fitting (regression analysis) and estimation of parameter values given a data sample.
*/

#ifndef ROOT_Fit_DataVectorfwd
#include "Fit/DataVectorfwd.h"
#endif

#ifndef ROOT_Fit_FitConfig
#include "Fit/FitConfig.h"
#endif

#ifndef ROOT_Fit_FitResult
#include "Fit/FitResult.h"
#endif

#ifndef ROOT_Math_IParamFunctionfwd
#include "Math/IParamFunctionfwd.h"
#endif

#include <memory>


namespace ROOT {


   namespace Math {
      class Minimizer;

      // should maybe put this in a FitMethodFunctionfwd file
      template<class FunctionType> class BasicFitMethodFunction;

      // define the normal and gradient function
      typedef BasicFitMethodFunction<ROOT::Math::IMultiGenFunction>  FitMethodFunction;
      typedef BasicFitMethodFunction<ROOT::Math::IMultiGradFunction> FitMethodGradFunction;

   }

   /**
      Namespace for the fitting classes
      @ingroup Fit
    */

   namespace Fit {

/**
   @defgroup FitMain User Fitting classes

   Main Classes used for fitting a given data set
   @ingroup Fit
*/

//___________________________________________________________________________________
/**
   Fitter class, entry point for performing all type of fits.
   Fits are performed using the generic ROOT::Fit::Fitter::Fit method.
   The inputs are the data points and a model function (using a ROOT::Math::IParamFunction)
   The result of the fit is returned and kept internally in the  ROOT::Fit::FitResult class.
   The configuration of the fit (parameters, options, etc...) are specified in the
   ROOT::Math::FitConfig class.
   After fitting the config of the fit will be modified to have the new values the resulting
   parameter of the fit with step sizes equal to the errors. FitConfig can be preserved with
   initial parameters by calling FitConfig.SetUpdateAfterFit(false);

   @ingroup FitMain
*/
class Fitter {

public:

   typedef ROOT::Math::IParamMultiFunction       IModelFunction;
   typedef ROOT::Math::IParamMultiGradFunction   IGradModelFunction;
   typedef ROOT::Math::IParamFunction            IModel1DFunction;
   typedef ROOT::Math::IParamGradFunction        IGradModel1DFunction;

   typedef ROOT::Math::IMultiGenFunction BaseFunc;
   typedef ROOT::Math::IMultiGradFunction BaseGradFunc;


   /**
      Default constructor
   */
   Fitter ();

   /**
      Destructor
   */
   ~Fitter ();

private:

   /**
      Copy constructor (disabled, class is not copyable)
   */
   Fitter(const Fitter &);

   /**
      Assignment operator (disabled, class is not copyable)
   */
   Fitter & operator = (const Fitter & rhs);


public:

   /**
       fit a data set using any  generic model  function
       If data set is binned a least square fit is performed
       If data set is unbinned a maximum likelihood fit (not extended) is done
       Pre-requisite on the function:
       it must implement the 1D or multidimensional parametric function interface
   */
   template < class Data , class Function>
   bool Fit( const Data & data, const Function & func ) {
      SetFunction(func);
      return Fit(data);
   }

   /**
       Fit a binned data set using a least square fit (default method)
   */
   bool Fit(const BinData & data) {
      return DoLeastSquareFit(data);
   }

   /**
       Fit a binned data set using a least square fit
   */
   bool LeastSquareFit(const BinData & data) {
      return DoLeastSquareFit(data);
   }

   /**
       fit an unbinned data set using loglikelihood method
   */
   bool Fit(const UnBinData & data, bool extended = false) {
      return DoLikelihoodFit(data, extended);
   }

   /**
      Likelihood fit (unbinned or unbinned) depending on the type of data
      If Binned default is extended
      If Unbinned default is NOT extended (for backward compatibility)
    */
   template <class Data>
   bool LikelihoodFit(const Data & data ) {
      return DoLikelihoodFit(data);
   }


   /**
      Likelihood fit using extended or not extended method
    */
   template <class Data>
   bool LikelihoodFit(const Data & data, bool extended ) {
      return DoLikelihoodFit(data, extended);
   }

   /**
       fit a data set using any  generic model  function
       Pre-requisite on the function:
   */
   template < class Data , class Function>
   bool LikelihoodFit( const Data & data, const Function & func, bool extended) {
      SetFunction(func);
      return DoLikelihoodFit(data, extended);
   }

   /**
      Fit using the a generic FCN function as a C++ callable object implementing
      double () (const double *)
      Note that the function dimension (i.e. the number of parameter) is needed in this case
      For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..)
    */
   template <class Function>
   bool FitFCN(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 the function dimension (i.e. the number of parameter) is needed in this case
      For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..)
    */
   template <class Function>
   bool SetFCN(unsigned int npar, Function  & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
      Fit using the given FCN function represented by a multi-dimensional function interface
      (ROOT::Math::IMultiGenFunction).
      Give optionally the initial arameter values, data size to have the fit Ndf correctly
      set in the FitResult and flag specifying if it is a chi2 fit.
      Note that if the parameters values are not given (params=0) the
      current parameter settings are used. The parameter settings can be created before
      by using the FitConfig::SetParamsSetting. If they have not been created they are created
      automatically when the params pointer is not zero.
      Note that passing a params != 0 will set the parameter settings to the new value AND also the
      step sizes to some pre-defined value (stepsize = 0.3 * abs(parameter_value) )
    */
   bool FitFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool
      chi2fit = false);

   /**
       Fit using a FitMethodFunction interface. Same as method above, but now extra information
       can be taken from the function class
   */
   bool FitFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0);

   /**
      Set the FCN function represented by a multi-dimensional function interface
      (ROOT::Math::IMultiGenFunction) and optionally the initial parameters
      See also note above for the initial parameters for FitFCN
    */
   bool SetFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
       Set the objective function (FCN)  using a FitMethodFunction interface.
       Same as method above, but now extra information can be taken from the function class
   */
   bool SetFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0);

   /**
      Fit using the given FCN function representing a multi-dimensional gradient function
      interface (ROOT::Math::IMultiGradFunction). In this case the minimizer will use the
      gradient information provided by the function.
      For the options same consideration as in the previous method
    */
   bool FitFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
       Fit using a FitMethodGradFunction interface. Same as method above, but now extra information
       can be taken from the function class
   */
   bool FitFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0);

   /**
      Set the FCN function represented by a multi-dimensional gradient function interface
      (ROOT::Math::IMultiGenFunction) and optionally the initial parameters
      See also note above for the initial parameters for FitFCN
    */
   bool SetFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
       Set the objective function (FCN)  using a FitMethodGradFunction interface.
       Same as method above, but now extra information can be taken from the function class
   */
   bool SetFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0);


   /**
      fit using user provided FCN with Minuit-like interface
      If npar = 0 it is assumed that the parameters are specified in the parameter settings created before
      For the options same consideration as in the previous method
    */
   typedef  void (* MinuitFCN_t )(int &npar, double *gin, double &f, double *u, int flag);
   bool FitFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
      set objective function using user provided FCN with Minuit-like interface
      If npar = 0 it is assumed that the parameters are specified in the parameter settings created before
      For the options same consideration as in the previous method
    */
   bool SetFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);

   /**
      Perform a fit with the previously set FCN function. Require SetFCN before
    */
   bool FitFCN();

   /**
      Perform a simple FCN evaluation. FitResult will be modified and contain  the value of the FCN
    */
   bool EvalFCN();


   /**
      do a linear fit on a set of bin-data
    */
   bool LinearFit(const BinData & data) { return DoLinearFit(data); }

   /**
       Set the fitted function (model function) from a parametric function interface
   */
   void  SetFunction(const IModelFunction & func, bool useGradient = false);
   /**
      Set the fitted function from a parametric 1D function interface
    */
   void  SetFunction(const IModel1DFunction & func, bool useGradient = false);

   /**
       Set the fitted function (model function) from a parametric gradient function interface
   */
   void  SetFunction(const IGradModelFunction & func, bool useGradient = true);
   /**
      Set the fitted function from 1D gradient parametric function interface
    */
   void  SetFunction(const IGradModel1DFunction & func, bool useGradient = true);


   /**
      get fit result
   */
   const FitResult & Result() const {
      assert( fResult.get() );
      return *fResult;
   }

   /**
      perform an error analysis on the result using the Hessian
      Errors are obtaied from the inverse of the Hessian matrix
      To be called only after fitting and when a minimizer supporting the Hessian calculations is used
      otherwise an error (false) is returned.
      A new  FitResult with the Hessian result will be produced
    */
   bool CalculateHessErrors();

   /**
      perform an error analysis on the result using MINOS
      To be called only after fitting and when a minimizer supporting MINOS is used
      otherwise an error (false) is returned.
      The result will be appended in the fit result class
      Optionally a vector of parameter indeces can be passed for selecting
      the parameters to analyse using FitConfig::SetMinosErrors
    */
   bool CalculateMinosErrors();

   /**
      access to the fit configuration (const method)
   */
   const FitConfig & Config() const { return fConfig; }

   /**
      access to the configuration (non const method)
   */
   FitConfig & Config() { return fConfig; }

   /**
      query if fit is binned. In cse of false teh fit can be unbinned
      or is not defined (like in case of fitting through a ::FitFCN)
    */
   bool IsBinFit() const { return fBinFit; }

   /**
      return pointer to last used minimizer
      (is NULL in case fit is not yet done)
      This pointer will be valid as far as the data, the objective function
      and the fitter class  have not been deleted.
      To be used only after fitting.
      The pointer should not be stored and will be invalided after performing a new fitting.
      In this case a new instance of ROOT::Math::Minimizer will be re-created and can be
      obtained calling again GetMinimizer()
    */
   ROOT::Math::Minimizer * GetMinimizer() const { return fMinimizer.get(); }

   /**
      return pointer to last used objective function
      (is NULL in case fit is not yet done)
      This pointer will be valid as far as the data and the fitter class
      have not been deleted. To be used after the fitting.
      The pointer should not be stored and will be invalided after performing a new fitting.
      In this case a new instance of the function pointer will be re-created and can be
      obtained calling again GetFCN()
    */
   ROOT::Math::IMultiGenFunction * GetFCN() const { return fObjFunction.get(); }


   /**
      apply correction in the error matrix for the weights for likelihood fits
      This method can be called only after a fit. The
      passed function (loglw2) is a log-likelihood function impelemented using the
      sum of weight squared
      When using FitConfig.SetWeightCorrection() this correction is applied
      automatically when doing a likelihood fit (binned or unbinned)
   */
   bool ApplyWeightCorrection(const ROOT::Math::IMultiGenFunction & loglw2, bool minimizeW2L=false);


protected:


   /// least square fit
   bool DoLeastSquareFit(const BinData & data);
   /// binned likelihood fit
   bool DoLikelihoodFit(const BinData & data, bool extended = true);
   /// un-binned likelihood fit
   bool DoLikelihoodFit(const UnBinData & data, bool extended = false);
   /// linear least square fit
   bool DoLinearFit(const BinData & data);

   // initialize the minimizer
   bool DoInitMinimizer();
   /// do minimization
   bool DoMinimization(const BaseFunc & f, const ROOT::Math::IMultiGenFunction * chifunc = 0);
   // do minimization after having set obj function
   bool DoMinimization(const ROOT::Math::IMultiGenFunction * chifunc = 0);
   // update config after fit
   void DoUpdateFitConfig();
   // get function calls from the FCN
   int GetNCallsFromFCN();

   // set 1D function
   void DoSetFunction(const IModel1DFunction & func, bool useGrad);
   // set generic N-d function
   void DoSetFunction(const IModelFunction & func, bool useGrad);

private:

   bool fUseGradient;       // flag to indicate if using gradient or not

   bool fBinFit;            // flag to indicate if fit is binned
                            // in case of false the fit is unbinned or undefined)
                            // flag it is used to compute chi2 for binned likelihood fit

   int fFitType;   // type of fit   (0 undefined, 1 least square, 2 likelihood)

   int fDataSize;  // size of data sets (need for Fumili or LM fitters)

   IModelFunction * fFunc;  // copy of the fitted  function containing on output the fit result (managed by FitResult)

   FitConfig fConfig;       // fitter configuration (options and parameter settings)

   std::auto_ptr<ROOT::Fit::FitResult>  fResult;  //! pointer to the object containing the result of the fit

   std::auto_ptr<ROOT::Math::Minimizer>  fMinimizer;  //! pointer to used minimizer

   std::auto_ptr<ROOT::Math::IMultiGenFunction>  fObjFunction;  //! pointer to used objective function


};

   } // end namespace Fit

} // end namespace ROOT

// implementation of inline methods


#ifndef __CINT__


#ifndef ROOT_Math_WrappedFunction
#include "Math/WrappedFunction.h"
#endif

template<class Function>
bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) {
   ROOT::Math::WrappedMultiFunction<Function &> wf(f,npar);
   return FitFCN(wf,par,datasize,chi2fit);
}
template<class Function>
bool ROOT::Fit::Fitter::SetFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) {
   ROOT::Math::WrappedMultiFunction<Function &> wf(f,npar);
   return SetFCN(wf,par,datasize,chi2fit);
}




#endif  // endif __CINT__

#endif /* ROOT_Fit_Fitter */
 Fitter.h:1
 Fitter.h:2
 Fitter.h:3
 Fitter.h:4
 Fitter.h:5
 Fitter.h:6
 Fitter.h:7
 Fitter.h:8
 Fitter.h:9
 Fitter.h:10
 Fitter.h:11
 Fitter.h:12
 Fitter.h:13
 Fitter.h:14
 Fitter.h:15
 Fitter.h:16
 Fitter.h:17
 Fitter.h:18
 Fitter.h:19
 Fitter.h:20
 Fitter.h:21
 Fitter.h:22
 Fitter.h:23
 Fitter.h:24
 Fitter.h:25
 Fitter.h:26
 Fitter.h:27
 Fitter.h:28
 Fitter.h:29
 Fitter.h:30
 Fitter.h:31
 Fitter.h:32
 Fitter.h:33
 Fitter.h:34
 Fitter.h:35
 Fitter.h:36
 Fitter.h:37
 Fitter.h:38
 Fitter.h:39
 Fitter.h:40
 Fitter.h:41
 Fitter.h:42
 Fitter.h:43
 Fitter.h:44
 Fitter.h:45
 Fitter.h:46
 Fitter.h:47
 Fitter.h:48
 Fitter.h:49
 Fitter.h:50
 Fitter.h:51
 Fitter.h:52
 Fitter.h:53
 Fitter.h:54
 Fitter.h:55
 Fitter.h:56
 Fitter.h:57
 Fitter.h:58
 Fitter.h:59
 Fitter.h:60
 Fitter.h:61
 Fitter.h:62
 Fitter.h:63
 Fitter.h:64
 Fitter.h:65
 Fitter.h:66
 Fitter.h:67
 Fitter.h:68
 Fitter.h:69
 Fitter.h:70
 Fitter.h:71
 Fitter.h:72
 Fitter.h:73
 Fitter.h:74
 Fitter.h:75
 Fitter.h:76
 Fitter.h:77
 Fitter.h:78
 Fitter.h:79
 Fitter.h:80
 Fitter.h:81
 Fitter.h:82
 Fitter.h:83
 Fitter.h:84
 Fitter.h:85
 Fitter.h:86
 Fitter.h:87
 Fitter.h:88
 Fitter.h:89
 Fitter.h:90
 Fitter.h:91
 Fitter.h:92
 Fitter.h:93
 Fitter.h:94
 Fitter.h:95
 Fitter.h:96
 Fitter.h:97
 Fitter.h:98
 Fitter.h:99
 Fitter.h:100
 Fitter.h:101
 Fitter.h:102
 Fitter.h:103
 Fitter.h:104
 Fitter.h:105
 Fitter.h:106
 Fitter.h:107
 Fitter.h:108
 Fitter.h:109
 Fitter.h:110
 Fitter.h:111
 Fitter.h:112
 Fitter.h:113
 Fitter.h:114
 Fitter.h:115
 Fitter.h:116
 Fitter.h:117
 Fitter.h:118
 Fitter.h:119
 Fitter.h:120
 Fitter.h:121
 Fitter.h:122
 Fitter.h:123
 Fitter.h:124
 Fitter.h:125
 Fitter.h:126
 Fitter.h:127
 Fitter.h:128
 Fitter.h:129
 Fitter.h:130
 Fitter.h:131
 Fitter.h:132
 Fitter.h:133
 Fitter.h:134
 Fitter.h:135
 Fitter.h:136
 Fitter.h:137
 Fitter.h:138
 Fitter.h:139
 Fitter.h:140
 Fitter.h:141
 Fitter.h:142
 Fitter.h:143
 Fitter.h:144
 Fitter.h:145
 Fitter.h:146
 Fitter.h:147
 Fitter.h:148
 Fitter.h:149
 Fitter.h:150
 Fitter.h:151
 Fitter.h:152
 Fitter.h:153
 Fitter.h:154
 Fitter.h:155
 Fitter.h:156
 Fitter.h:157
 Fitter.h:158
 Fitter.h:159
 Fitter.h:160
 Fitter.h:161
 Fitter.h:162
 Fitter.h:163
 Fitter.h:164
 Fitter.h:165
 Fitter.h:166
 Fitter.h:167
 Fitter.h:168
 Fitter.h:169
 Fitter.h:170
 Fitter.h:171
 Fitter.h:172
 Fitter.h:173
 Fitter.h:174
 Fitter.h:175
 Fitter.h:176
 Fitter.h:177
 Fitter.h:178
 Fitter.h:179
 Fitter.h:180
 Fitter.h:181
 Fitter.h:182
 Fitter.h:183
 Fitter.h:184
 Fitter.h:185
 Fitter.h:186
 Fitter.h:187
 Fitter.h:188
 Fitter.h:189
 Fitter.h:190
 Fitter.h:191
 Fitter.h:192
 Fitter.h:193
 Fitter.h:194
 Fitter.h:195
 Fitter.h:196
 Fitter.h:197
 Fitter.h:198
 Fitter.h:199
 Fitter.h:200
 Fitter.h:201
 Fitter.h:202
 Fitter.h:203
 Fitter.h:204
 Fitter.h:205
 Fitter.h:206
 Fitter.h:207
 Fitter.h:208
 Fitter.h:209
 Fitter.h:210
 Fitter.h:211
 Fitter.h:212
 Fitter.h:213
 Fitter.h:214
 Fitter.h:215
 Fitter.h:216
 Fitter.h:217
 Fitter.h:218
 Fitter.h:219
 Fitter.h:220
 Fitter.h:221
 Fitter.h:222
 Fitter.h:223
 Fitter.h:224
 Fitter.h:225
 Fitter.h:226
 Fitter.h:227
 Fitter.h:228
 Fitter.h:229
 Fitter.h:230
 Fitter.h:231
 Fitter.h:232
 Fitter.h:233
 Fitter.h:234
 Fitter.h:235
 Fitter.h:236
 Fitter.h:237
 Fitter.h:238
 Fitter.h:239
 Fitter.h:240
 Fitter.h:241
 Fitter.h:242
 Fitter.h:243
 Fitter.h:244
 Fitter.h:245
 Fitter.h:246
 Fitter.h:247
 Fitter.h:248
 Fitter.h:249
 Fitter.h:250
 Fitter.h:251
 Fitter.h:252
 Fitter.h:253
 Fitter.h:254
 Fitter.h:255
 Fitter.h:256
 Fitter.h:257
 Fitter.h:258
 Fitter.h:259
 Fitter.h:260
 Fitter.h:261
 Fitter.h:262
 Fitter.h:263
 Fitter.h:264
 Fitter.h:265
 Fitter.h:266
 Fitter.h:267
 Fitter.h:268
 Fitter.h:269
 Fitter.h:270
 Fitter.h:271
 Fitter.h:272
 Fitter.h:273
 Fitter.h:274
 Fitter.h:275
 Fitter.h:276
 Fitter.h:277
 Fitter.h:278
 Fitter.h:279
 Fitter.h:280
 Fitter.h:281
 Fitter.h:282
 Fitter.h:283
 Fitter.h:284
 Fitter.h:285
 Fitter.h:286
 Fitter.h:287
 Fitter.h:288
 Fitter.h:289
 Fitter.h:290
 Fitter.h:291
 Fitter.h:292
 Fitter.h:293
 Fitter.h:294
 Fitter.h:295
 Fitter.h:296
 Fitter.h:297
 Fitter.h:298
 Fitter.h:299
 Fitter.h:300
 Fitter.h:301
 Fitter.h:302
 Fitter.h:303
 Fitter.h:304
 Fitter.h:305
 Fitter.h:306
 Fitter.h:307
 Fitter.h:308
 Fitter.h:309
 Fitter.h:310
 Fitter.h:311
 Fitter.h:312
 Fitter.h:313
 Fitter.h:314
 Fitter.h:315
 Fitter.h:316
 Fitter.h:317
 Fitter.h:318
 Fitter.h:319
 Fitter.h:320
 Fitter.h:321
 Fitter.h:322
 Fitter.h:323
 Fitter.h:324
 Fitter.h:325
 Fitter.h:326
 Fitter.h:327
 Fitter.h:328
 Fitter.h:329
 Fitter.h:330
 Fitter.h:331
 Fitter.h:332
 Fitter.h:333
 Fitter.h:334
 Fitter.h:335
 Fitter.h:336
 Fitter.h:337
 Fitter.h:338
 Fitter.h:339
 Fitter.h:340
 Fitter.h:341
 Fitter.h:342
 Fitter.h:343
 Fitter.h:344
 Fitter.h:345
 Fitter.h:346
 Fitter.h:347
 Fitter.h:348
 Fitter.h:349
 Fitter.h:350
 Fitter.h:351
 Fitter.h:352
 Fitter.h:353
 Fitter.h:354
 Fitter.h:355
 Fitter.h:356
 Fitter.h:357
 Fitter.h:358
 Fitter.h:359
 Fitter.h:360
 Fitter.h:361
 Fitter.h:362
 Fitter.h:363
 Fitter.h:364
 Fitter.h:365
 Fitter.h:366
 Fitter.h:367
 Fitter.h:368
 Fitter.h:369
 Fitter.h:370
 Fitter.h:371
 Fitter.h:372
 Fitter.h:373
 Fitter.h:374
 Fitter.h:375
 Fitter.h:376
 Fitter.h:377
 Fitter.h:378
 Fitter.h:379
 Fitter.h:380
 Fitter.h:381
 Fitter.h:382
 Fitter.h:383
 Fitter.h:384
 Fitter.h:385
 Fitter.h:386
 Fitter.h:387
 Fitter.h:388
 Fitter.h:389
 Fitter.h:390
 Fitter.h:391
 Fitter.h:392
 Fitter.h:393
 Fitter.h:394
 Fitter.h:395
 Fitter.h:396
 Fitter.h:397
 Fitter.h:398
 Fitter.h:399
 Fitter.h:400
 Fitter.h:401
 Fitter.h:402
 Fitter.h:403
 Fitter.h:404
 Fitter.h:405
 Fitter.h:406
 Fitter.h:407
 Fitter.h:408
 Fitter.h:409
 Fitter.h:410
 Fitter.h:411
 Fitter.h:412
 Fitter.h:413
 Fitter.h:414
 Fitter.h:415
 Fitter.h:416
 Fitter.h:417
 Fitter.h:418
 Fitter.h:419
 Fitter.h:420
 Fitter.h:421
 Fitter.h:422
 Fitter.h:423
 Fitter.h:424
 Fitter.h:425
 Fitter.h:426
 Fitter.h:427
 Fitter.h:428
 Fitter.h:429
 Fitter.h:430
 Fitter.h:431
 Fitter.h:432
 Fitter.h:433
 Fitter.h:434
 Fitter.h:435
 Fitter.h:436
 Fitter.h:437
 Fitter.h:438
 Fitter.h:439
 Fitter.h:440
 Fitter.h:441
 Fitter.h:442
 Fitter.h:443
 Fitter.h:444
 Fitter.h:445
 Fitter.h:446
 Fitter.h:447
 Fitter.h:448
 Fitter.h:449
 Fitter.h:450
 Fitter.h:451
 Fitter.h:452
 Fitter.h:453
 Fitter.h:454
 Fitter.h:455
 Fitter.h:456
 Fitter.h:457
 Fitter.h:458
 Fitter.h:459
 Fitter.h:460
 Fitter.h:461
 Fitter.h:462
 Fitter.h:463
 Fitter.h:464
 Fitter.h:465
 Fitter.h:466
 Fitter.h:467
 Fitter.h:468
 Fitter.h:469
 Fitter.h:470
 Fitter.h:471
 Fitter.h:472
 Fitter.h:473
 Fitter.h:474
 Fitter.h:475
 Fitter.h:476