rf902_numgenconfig.C File Reference

Detailed Description

'NUMERIC ALGORITHM TUNING' RooFit tutorial macro #902

Configuration and customization of how MC sampling algorithms on specific p.d.f.s are executed

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Processing /mnt/vdb/lsf/workspace/root-makedoc-v608/rootspi/rdoc/src/v6-08-00-patches/tutorials/roofit/rf902_numgenconfig.C...

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt

 --- RooGenContext --- 
Using PDF RooChebychev::model[ x=x coefList=(0,0.5,-0.1) ]
Use PDF generator for ()
Use MC sampling generator RooAcceptReject for (x)
RooDataSet::modelData[x] = 10000 entries
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
F                                                                              F
F                   ****************************************                   F
F                    ******      TFoam::Initialize    ******                   F
F                   ****************************************                   F
F                                                      TFOAM                   F
F    Version =           1.02M   =                   Release date:  2005.04.10 F
F       kDim =          1 =  Dimension of the hyper-cubical space              F
F     nCells =         30 =  Requested number of Cells (half of them active)   F
F     nSampl =        200 =  No of MC events in exploration of a cell          F
F       nBin =          8 =  No of bins in histograms, MC exploration of cell  F
F   EvPerBin =         25 =  Maximum No effective_events/bin, MC exploration   F
F   OptDrive =          2 =  Type of Driver   =1,2 for Sigma,WtMax             F
F     OptRej =          1 =  MC rejection on/off for OptRej=0,1                F
F   MaxWtRej =             1.1   =       Maximum wt in rejection for wt=1 evts F
F                                                                              F
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
11
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
F                                                                              F
F                    ***  TFoam::Initialize FINISHED!!!  ***                   F
F     nCalls =       5800 =            Total number of function calls          F
F     XPrime =       0.1100962   =     Primary total integral                  F
F     XDiver =     0.010134446   =     Driver  total integral                  F
F   mcResult =     0.099961756   =     Estimate of the true MC Integral        F
F                                                                              F
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
 --- RooGenContext --- 
Using PDF RooChebychev::model[ x=x coefList=(0,0.5,-0.1) ]
Use PDF generator for ()
Use MC sampling generator RooFoamGenerator for (x)
RooDataSet::modelData[x] = 10000 entries

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooConstVar.h"
#include "RooChebychev.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "RooNumGenConfig.h"
#include "RooArgSet.h"
#include <iomanip>
using namespace RooFit ;


void rf902_numgenconfig()
{

   // A d j u s t   g l o b a l   MC   s a m p l i n g   s t r a t e g y 
   // ------------------------------------------------------------------

   // Example p.d.f. for use below
   RooRealVar x("x","x",0,10) ;
   RooChebychev model("model","model",x,RooArgList(RooConst(0),RooConst(0.5),RooConst(-0.1))) ;


   // Change global strategy for 1D sampling problems without conditional observable
   // (1st kFALSE) and without discrete observable (2nd kFALSE) from RooFoamGenerator,
   // ( an interface to the TFoam MC generator with adaptive subdivisioning strategy ) to RooAcceptReject,
   // a plain accept/reject sampling algorithm [ RooFit default before ROOT 5.23/04 ]
   RooAbsPdf::defaultGeneratorConfig()->method1D(kFALSE,kFALSE).setLabel("RooAcceptReject") ;

   // Generate 10Kevt using RooAcceptReject
   RooDataSet* data_ar = model.generate(x,10000,Verbose(kTRUE)) ;
   data_ar->Print() ;


   // A d j u s t i n g   d e f a u l t   c o n f i g   f o r   a   s p e c i f i c   p d f 
   // -------------------------------------------------------------------------------------
   
   // Another possibility: associate custom MC sampling configuration as default for object 'model'
   // The kTRUE argument will install a clone of the default configuration as specialized configuration
   // for this model if none existed so far
   model.specialGeneratorConfig(kTRUE)->method1D(kFALSE,kFALSE).setLabel("RooFoamGenerator") ;


   // A d j u s t i n g   p a r a m e t e r s   o f   a   s p e c i f i c   t e c h n i q u e 
   // ---------------------------------------------------------------------------------------

   // Adjust maximum number of steps of RooIntegrator1D in the global default configuration
   RooAbsPdf::defaultGeneratorConfig()->getConfigSection("RooAcceptReject").setRealValue("nTrial1D",2000) ;

   
   // Example of how to change the parameters of a numeric integrator
   // (Each config section is a RooArgSet with RooRealVars holding real-valued parameters
   //  and RooCategories holding parameters with a finite set of options)
   model.specialGeneratorConfig()->getConfigSection("RooFoamGenerator").setRealValue("chatLevel",1) ;

   // Generate 10Kevt using RooFoamGenerator (FOAM verbosity increased with above chatLevel adjustment for illustration purposes)
   RooDataSet* data_foam = model.generate(x,10000,Verbose()) ;
   data_foam->Print() ;


}

Author

07/2008 - Wouter Verkerke

Definition in file rf902_numgenconfig.C.