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rf707_kernelestimation.C
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1/// \file
2/// \ingroup tutorial_roofit
3/// \notebook
4/// 'SPECIAL PDFS' RooFit tutorial macro #707
5///
6/// Using non-parametric (multi-dimensional) kernel estimation p.d.f.s
7///
8/// \macro_image
9/// \macro_output
10/// \macro_code
11/// \author 07/2008 - Wouter Verkerke
12
13
14#include "RooRealVar.h"
15#include "RooDataSet.h"
16#include "RooGaussian.h"
17#include "RooConstVar.h"
18#include "RooPolynomial.h"
19#include "RooKeysPdf.h"
20#include "RooNDKeysPdf.h"
21#include "RooProdPdf.h"
22#include "TCanvas.h"
23#include "TAxis.h"
24#include "TH1.h"
25#include "RooPlot.h"
26using namespace RooFit ;
27
28
30{
31 // C r e a t e l o w s t a t s 1 - D d a t a s e t
32 // -------------------------------------------------------
33
34 // Create a toy pdf for sampling
35 RooRealVar x("x","x",0,20) ;
36 RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
37
38 // Sample 500 events from p
39 RooDataSet* data1 = p.generate(x,200) ;
40
41
42
43 // C r e a t e 1 - D k e r n e l e s t i m a t i o n p d f
44 // ---------------------------------------------------------------
45
46 // Create adaptive kernel estimation pdf. In this configuration the input data
47 // is mirrored over the boundaries to minimize edge effects in distribution
48 // that do not fall to zero towards the edges
49 RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ;
50
51 // An adaptive kernel estimation pdf on the same data without mirroring option
52 // for comparison
53 RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ;
54
55
56 // Adaptive kernel estimation pdf with increased bandwidth scale factor
57 // (promotes smoothness over detail preservation)
58 RooKeysPdf kest3("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth,2) ;
59
60
61 // Plot kernel estimation pdfs with and without mirroring over data
62 RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;
63 data1->plotOn(frame) ;
64 kest1.plotOn(frame) ;
65 kest2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
66
67
68 // Plot kernel estimation pdfs with regular and increased bandwidth
69 RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ;
70 kest1.plotOn(frame2) ;
71 kest3.plotOn(frame2,LineColor(kMagenta)) ;
72
73
74
75 // C r e a t e l o w s t a t s 2 - D d a t a s e t
76 // -------------------------------------------------------
77
78 // Construct a 2D toy pdf for sampling
79 RooRealVar y("y","y",0,20) ;
80 RooPolynomial py("py","py",y,RooArgList(RooConst(0.01),RooConst(0.01),RooConst(-0.0004))) ;
81 RooProdPdf pxy("pxy","pxy",RooArgSet(p,py)) ;
82 RooDataSet* data2 = pxy.generate(RooArgSet(x,y),1000) ;
83
84
85
86 // C r e a t e 2 - D k e r n e l e s t i m a t i o n p d f
87 // ---------------------------------------------------------------
88
89 // Create 2D adaptive kernel estimation pdf with mirroring
90 RooNDKeysPdf kest4("kest4","kest4",RooArgSet(x,y),*data2,"am") ;
91
92 // Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth
93 RooNDKeysPdf kest5("kest5","kest5",RooArgSet(x,y),*data2,"am",2) ;
94
95 // Create a histogram of the data
96 TH1* hh_data = data2->createHistogram("hh_data",x,Binning(10),YVar(y,Binning(10))) ;
97
98 // Create histogram of the 2d kernel estimation pdfs
99 TH1* hh_pdf = kest4.createHistogram("hh_pdf",x,Binning(25),YVar(y,Binning(25))) ;
100 TH1* hh_pdf2 = kest5.createHistogram("hh_pdf2",x,Binning(25),YVar(y,Binning(25))) ;
101 hh_pdf->SetLineColor(kBlue) ;
102 hh_pdf2->SetLineColor(kMagenta) ;
103
104
105
106 TCanvas* c = new TCanvas("rf707_kernelestimation","rf707_kernelestimation",800,800) ;
107 c->Divide(2,2) ;
108 c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
109 c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
110 c->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;
111 c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.4) ; hh_pdf->Draw("surf") ; hh_pdf2->Draw("surfsame") ;
112
113}
#define c(i)
Definition: RSha256.hxx:101
@ kRed
Definition: Rtypes.h:63
@ kMagenta
Definition: Rtypes.h:63
@ kBlue
Definition: Rtypes.h:63
@ kDashed
Definition: TAttLine.h:48
#define gPad
Definition: TVirtualPad.h:286
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Calls RooPlot* plotOn(RooPlot* frame, const RooLinkedList& cmdList) const ;.
Definition: RooAbsData.cxx:531
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:31
TH2F * createHistogram(const RooAbsRealLValue &var1, const RooAbsRealLValue &var2, const char *cuts="", const char *name="hist") const
Create a TH2F histogram of the distribution of the specified variable using this dataset.
Class RooKeysPdf implements a one-dimensional kernel estimation p.d.f which model the distribution of...
Definition: RooKeysPdf.h:25
Generic N-dimensional implementation of a kernel estimation p.d.f.
Definition: RooNDKeysPdf.h:48
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:41
TAxis * GetYaxis() const
Definition: RooPlot.cxx:1123
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:558
RooPolynomial implements a polynomial p.d.f of the form.
Definition: RooPolynomial.h:28
RooProdPdf is an efficient implementation of a product of PDFs of the form.
Definition: RooProdPdf.h:31
RooRealVar represents a fundamental (non-derived) real valued object.
Definition: RooRealVar.h:36
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title Offset is a correction factor with respect to the "s...
Definition: TAttAxis.cxx:294
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition: TAttLine.h:40
The Canvas class.
Definition: TCanvas.h:31
The TH1 histogram class.
Definition: TH1.h:56
TAxis * GetZaxis()
Definition: TH1.h:318
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2974
Double_t y[n]
Definition: legend1.C:17
Double_t x[n]
Definition: legend1.C:17
RooCmdArg Binning(const RooAbsBinning &binning)
RooCmdArg YVar(const RooAbsRealLValue &var, const RooCmdArg &arg=RooCmdArg::none())
RooConstVar & RooConst(Double_t val)
RooCmdArg LineColor(Color_t color)
RooCmdArg Bins(Int_t nbin)
RooCmdArg LineStyle(Style_t style)
const char * Title
Definition: TXMLSetup.cxx:67