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Reference Guide
DistSampler.h
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1// @(#)root/mathcore:$Id$
2// Author: L. Moneta Fri Sep 22 15:06:47 2006
3
4/**********************************************************************
5 * *
6 * Copyright (c) 2006 LCG ROOT Math Team, CERN/PH-SFT *
7 * *
8 * *
9 **********************************************************************/
10
11// Header file for class DistSampler
12
13#ifndef ROOT_Math_DistSampler
14#define ROOT_Math_DistSampler
15
16#include "Math/IFunctionfwd.h"
17
19
20#include <vector>
21#include <cassert>
22
23class TRandom;
24
25namespace ROOT {
26
27 namespace Fit {
28
29 class DataRange;
30 class BinData;
31 class UnBinData;
32 }
33
34 namespace Math {
35
36 class DistSamplerOptions;
37
38/**
39 @defgroup Random Random Classes
40
41 Pseudo-random numbers generator classes and for generation of random number distributions.
42 These classes implement several pseudo-random number generators and method for generation of random numbers
43 according to arbitrary distributions
44
45 @ingroup MathCore
46
47*/
48
49/**
50 Interface class for generic sampling of a distribution,
51 i.e. generating random numbers according to arbitrary distributions
52
53 @ingroup Random
54*/
55
56
58
59public:
60
61 /// default constructor
62 DistSampler() : fOwnFunc(false), fRange(0), fFunc(0) {}
63
64
65 /// virtual destructor
66 virtual ~DistSampler();
67
68
69
70 /// set the parent function distribution to use for sampling (generic case)
71 template<class Function>
72 void SetFunction(Function & func, unsigned int dim) {
74 fData.resize(dim);
75 // need to clone to avoid temporary
76 DoSetFunction(wf,true);
77 }
78
79 /// set the parent function distribution to use for random sampling (one dim case)
80 virtual void SetFunction(const ROOT::Math::IGenFunction & func) {
81 SetFunction<const ROOT::Math::IGenFunction>(func, 1);
82 }
83
84
85 /// set the parent function distribution to use for random sampling (multi-dim case)
86 virtual void SetFunction(const ROOT::Math::IMultiGenFunction & func) {
87 DoSetFunction(func,false);
88 }
89
90 /// return the dimension of the parent distribution (and the data)
91 unsigned int NDim() const { return fData.size(); }
92
93
94 /**
95 initialize the generators with the given algorithm
96 Implemented by derived classes who needs it
97 (like UnuranSampler)
98 If nothing is specified use default algorithm
99 from DistSamplerOptions::SetDefaultAlgorithm
100 */
101 virtual bool Init(const char * =""/* algorithm */) { return true;}
102
103 /**
104 initialize the generators with the given option
105 which my include the algorithm but also more if
106 the method is re-impelmented by derived class
107 The default implementation calls the above method
108 passing just the algorithm name
109 */
110 virtual bool Init(const DistSamplerOptions & opt );
111
112
113 /**
114 Set the random engine to be used
115 To be implemented by the derived classes who provides
116 random sampling
117 */
118 virtual void SetRandom(TRandom * ) {}
119
120 /**
121 Set the random seed for the TRandom instances used by the sampler
122 classes
123 To be implemented by the derived classes who provides random sampling
124 */
125 virtual void SetSeed(unsigned int /*seed*/ ) {}
126
127 /**
128 Get the random engine used by the sampler
129 To be implemented by the derived classes who needs it
130 Returns zero by default
131 */
132 virtual TRandom * GetRandom() { return 0; }
133
134 /// set range in a given dimension
135 void SetRange(double xmin, double xmax, int icoord = 0);
136
137 /// set range for all dimensions
138 void SetRange(const double * xmin, const double * xmax);
139
140 /// set range using DataRange class
141 void SetRange(const ROOT::Fit::DataRange & range);
142
143 /// set the mode of the distribution (could be useful to some methods)
144 /// implemented by derived classes if needed
145 virtual void SetMode(double ) {}
146
147 /// set the normalization area of distribution
148 /// implemented by derived classes if needed
149 virtual void SetArea(double) {}
150
151 /// get the parent distribution function (must be called after setting the function)
153 return *fFunc;
154 }
155
156
157 /**
158 sample one event in one dimension
159 better implementation could be provided by the derived classes
160 */
161 virtual double Sample1D() {
162 Sample(&fData[0]);
163 return fData[0];
164 }
165
166 /**
167 sample one event and rerturning array x with coordinates
168 */
169 const double * Sample() {
170 Sample(&fData[0]);
171 return &fData.front();
172 }
173
174 /**
175 sample one event in multi-dimension by filling the given array
176 return false if sampling failed
177 */
178 virtual bool Sample(double * x) = 0;
179
180 /**
181 sample one bin given an estimated of the pdf in the bin
182 (this can be function value at the center or its integral in the bin
183 divided by the bin width)
184 By default do not do random sample, just return the function values
185 Typically Poisson statistics will be used
186 */
187 virtual bool SampleBin(double prob, double & value, double * error = 0) {
188 value = prob;
189 if (error) *error = 0;
190 return true;
191 }
192 /**
193 sample a set of bins given a vector of probabilities
194 Typically multinomial statistics will be used and the sum of the probabilities
195 will be equal to the total number of events to be generated
196 For sampling the bins indipendently, SampleBin should be used
197 */
198 virtual bool SampleBins(unsigned int n, const double * prob, double * values, double * errors = 0) {
199 std::copy(prob,prob+n, values);
200 if (errors) std::fill(errors,errors+n,0);
201 return true;
202 }
203
204
205 /**
206 generate a un-binned data sets (fill the given data set)
207 if dataset has already data append to it
208 */
209 virtual bool Generate(unsigned int nevt, ROOT::Fit::UnBinData & data);
210
211
212 /**
213 generate a bin data set .
214 A range must have been set before (otherwise inf is returned)
215 and the bins are equidinstant in the previously defined range
216 bin center values must be present in given data set
217 If the sampler is implemented by a random one, the entries
218 will be binned according to the Poisson distribution
219 It is assumed the distribution is normalized, otherwise the nevt must be scaled
220 accordingly. The expected value/bin nexp = f(x_i) * binArea/ nevt
221 Extend control if use a fixed (i.e. multinomial statistics) or floating total number of events
222 */
223 virtual bool Generate(unsigned int nevt, const int * nbins, ROOT::Fit::BinData & data, bool extend = true);
224 /**
225 same as before but passing the range in case of 1 dim data
226 */
227 bool Generate(unsigned int nevt, int nbins, double xmin, double xmax, ROOT::Fit::BinData & data, bool extend = true) {
229 int nbs[1]; nbs[0] = nbins;
230 return Generate(nevt, nbs, data, extend);
231 }
232
233
234protected:
235
236 // internal method to set the function
237 virtual void DoSetFunction(const ROOT::Math::IMultiGenFunction & func, bool copy);
238 // check if generator have been initialized correctly and one can start generating
239 bool IsInitialized() ;
240 /// return the data range of the Pdf . Must be called after setting the function
242 assert(fRange);
243 return *fRange;
244 }
245
246
247
248
249private:
250
251 // private methods
252
253 bool fOwnFunc; // flag to indicate if the function is owned
254 mutable std::vector<double> fData; // internal array used to cached the sample data
256 const ROOT::Math::IMultiGenFunction * fFunc; // internal function (ND)
257
258
259};
260
261 } // end namespace Math
262
263} // end namespace ROOT
264
265
266#endif /* ROOT_Math_DistSampler */
float xmin
Definition: THbookFile.cxx:93
float xmax
Definition: THbookFile.cxx:93
Double_t(* Function)(Double_t)
Definition: Functor.C:4
Class describing the binned data sets : vectors of x coordinates, y values and optionally error on y ...
Definition: BinData.h:53
class describing the range in the coordinates it supports multiple range in a coordinate.
Definition: DataRange.h:34
Class describing the unbinned data sets (just x coordinates values) of any dimensions.
Definition: UnBinData.h:42
DistSampler options class.
Interface class for generic sampling of a distribution, i.e.
Definition: DistSampler.h:57
const double * Sample()
sample one event and rerturning array x with coordinates
Definition: DistSampler.h:169
virtual bool SampleBin(double prob, double &value, double *error=0)
sample one bin given an estimated of the pdf in the bin (this can be function value at the center or ...
Definition: DistSampler.h:187
virtual bool Sample(double *x)=0
sample one event in multi-dimension by filling the given array return false if sampling failed
ROOT::Fit::DataRange * fRange
Definition: DistSampler.h:255
virtual void DoSetFunction(const ROOT::Math::IMultiGenFunction &func, bool copy)
Definition: DistSampler.cxx:63
const ROOT::Math::IMultiGenFunction & ParentPdf() const
get the parent distribution function (must be called after setting the function)
Definition: DistSampler.h:152
bool Generate(unsigned int nevt, int nbins, double xmin, double xmax, ROOT::Fit::BinData &data, bool extend=true)
same as before but passing the range in case of 1 dim data
Definition: DistSampler.h:227
unsigned int NDim() const
return the dimension of the parent distribution (and the data)
Definition: DistSampler.h:91
virtual void SetFunction(const ROOT::Math::IGenFunction &func)
set the parent function distribution to use for random sampling (one dim case)
Definition: DistSampler.h:80
virtual ~DistSampler()
virtual destructor
Definition: DistSampler.cxx:29
virtual bool Generate(unsigned int nevt, ROOT::Fit::UnBinData &data)
generate a un-binned data sets (fill the given data set) if dataset has already data append to it
Definition: DistSampler.cxx:95
const ROOT::Math::IMultiGenFunction * fFunc
Definition: DistSampler.h:256
DistSampler()
default constructor
Definition: DistSampler.h:62
void SetRange(double xmin, double xmax, int icoord=0)
set range in a given dimension
Definition: DistSampler.cxx:40
const ROOT::Fit::DataRange & PdfRange() const
return the data range of the Pdf . Must be called after setting the function
Definition: DistSampler.h:241
virtual void SetFunction(const ROOT::Math::IMultiGenFunction &func)
set the parent function distribution to use for random sampling (multi-dim case)
Definition: DistSampler.h:86
void SetFunction(Function &func, unsigned int dim)
set the parent function distribution to use for sampling (generic case)
Definition: DistSampler.h:72
virtual TRandom * GetRandom()
Get the random engine used by the sampler To be implemented by the derived classes who needs it Retur...
Definition: DistSampler.h:132
virtual bool Init(const char *="")
initialize the generators with the given algorithm Implemented by derived classes who needs it (like ...
Definition: DistSampler.h:101
virtual void SetArea(double)
set the normalization area of distribution implemented by derived classes if needed
Definition: DistSampler.h:149
virtual double Sample1D()
sample one event in one dimension better implementation could be provided by the derived classes
Definition: DistSampler.h:161
virtual void SetRandom(TRandom *)
Set the random engine to be used To be implemented by the derived classes who provides random samplin...
Definition: DistSampler.h:118
virtual bool SampleBins(unsigned int n, const double *prob, double *values, double *errors=0)
sample a set of bins given a vector of probabilities Typically multinomial statistics will be used an...
Definition: DistSampler.h:198
std::vector< double > fData
Definition: DistSampler.h:254
virtual void SetMode(double)
set the mode of the distribution (could be useful to some methods) implemented by derived classes if ...
Definition: DistSampler.h:145
virtual void SetSeed(unsigned int)
Set the random seed for the TRandom instances used by the sampler classes To be implemented by the de...
Definition: DistSampler.h:125
Documentation for the abstract class IBaseFunctionMultiDim.
Definition: IFunction.h:62
Interface (abstract class) for generic functions objects of one-dimension Provides a method to evalua...
Definition: IFunction.h:135
Template class to wrap any C++ callable object implementing operator() (const double * x) in a multi-...
This is the base class for the ROOT Random number generators.
Definition: TRandom.h:27
Double_t x[n]
Definition: legend1.C:17
const Int_t n
Definition: legend1.C:16
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
Namespace for new Math classes and functions.
Namespace for new ROOT classes and functions.
Definition: StringConv.hxx:21
fill
Definition: fit1_py.py:6