#include <iostream>
#include <assert.h>
#include <time.h>
#include <chrono>
#include <random>
#include <algorithm>
#include <tclap/CmdLine.h>
#include <hydra/device/System.h>
#include <hydra/Function.h>
#include <hydra/Lambda.h>
#include <hydra/Random.h>
#include <hydra/LogLikelihoodFCN.h>
#include <hydra/Parameter.h>
#include <hydra/UserParameters.h>
#include <hydra/Pdf.h>
#include <hydra/AddPdf.h>
#include <hydra/Filter.h>
#include <hydra/DenseHistogram.h>
#include <hydra/functions/Gaussian.h>
#include <hydra/functions/Exponential.h>
#include <hydra/RandomFill.h>
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/MnPrint.h"
#include "Minuit2/MnMigrad.h"
#include "Minuit2/MnMinimize.h"
Go to the source code of this file.
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cmd | add (EArg) |
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| catch (TCLAP::ArgException &e) |
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| declarg (_X, double) int main(int argv |
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TCLAP::ValueArg< size_t > | EArg ("n", "number-of-events","Number of events", true, 10e6, "size_t") |
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Hist_Data | Fill (range.begin(), range.end()) |
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std::cout<< "Minimum: "<< minimum_d<< std::endl;std::cout<< "-----------------------------------------"<< std::endl;std::cout<< "| [Fit] GPU Time (ms) ="<< elapsed_d.count()<< std::endl;std::cout<< "-----------------------------------------"<< std::endl;hydra::DenseHistogram< double, 1, hydra::device::sys_t > | Hist_Data (100, min, max) |
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cmd | parse (argv, argc) |
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model | SetExtended (1) |
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char ** | argc |
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std::cout<< std::endl<< "Generated data:"<< std::endl;for(size_t i=0;i< 10;i++) std::cout<< "["<< i<< "] :"<< data_d[i]<< std::endl;auto filter=hydra::wrap_lambda([=] __hydra_dual__(double x){ return(x > min) &&(x< max);});auto range=hydra::filter(data_d, filter);std::cout<< std::endl<< "Filtered data:"<< std::endl;for(size_t i=0;i< 10;i++) std::cout<< "["<< i<< "] :"<< range.begin()[i]<< std::endl;auto fcn=hydra::make_loglikehood_fcn(model, range);ROOT::Minuit2::MnPrint::SetGlobalLevel(3);hydra::Print::SetLevel(hydra::WARNING);MnStrategy strategy(2);MnMigrad migrad_d(fcn, fcn.GetParameters().GetMnState(), strategy);std::cout<< fcn.GetParameters().GetMnState()<< std::endl;auto start_d=std::chrono::high_resolution_clock::now();FunctionMinimum minimum_d=FunctionMinimum(migrad_d(50000, 5));auto end_d=std::chrono::high_resolution_clock::now();std::chrono::duration< double, std::milli > | elapsed_d = end_d - start_d |
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auto | Exp_PDF |
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auto | Gauss1_PDF |
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auto | Gauss2_PDF |
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double | max = 10.0 |
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hydra::Parameter | mean1_p = hydra::Parameter::Create().Name("Mean_1").Value( 2.5) .Error(0.0001).Limits(0.0, 10.0) |
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hydra::Parameter | mean2_p = hydra::Parameter::Create().Name("Mean_2").Value(5.0) .Error(0.0001).Limits(0.0, 10.0) |
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double | min = 0.0 |
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auto | model = hydra::add_pdfs( {N_Gauss_1_p, N_Gauss_2_p, N_Exp_p }, Gauss1_PDF, Gauss2_PDF, Exp_PDF) |
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hydra::Parameter | N_Exp_p ("N_Exp", nentries, sqrt(nentries), nentries-nentries/2, nentries+nentries/2) |
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hydra::Parameter | N_Gauss_1_p ("N_Gauss1", nentries, sqrt(nentries), nentries-nentries/2, nentries+nentries/2) |
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hydra::Parameter | N_Gauss_2_p ("N_Gauss2", nentries, sqrt(nentries), nentries-nentries/2, nentries+nentries/2) |
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| nentries = EArg.getValue() |
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| return |
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hydra::Parameter | sigma1_p = hydra::Parameter::Create().Name("Sigma_1").Value(0.5).Error(0.0001).Limits(0.01, 1.5) |
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hydra::Parameter | sigma2_p = hydra::Parameter::Create().Name("Sigma_2").Value(0.5).Error(0.0001).Limits(0.01, 1.5) |
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hydra::Parameter | tau_p = hydra::Parameter::Create().Name("Tau").Value(1.0).Error(0.0001).Limits(-2.0, 2.0) |
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| try |
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◆ EXTENDED_LOGLL_FIT_INL_
#define EXTENDED_LOGLL_FIT_INL_ |
◆ add()
◆ catch()
catch |
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TCLAP::ArgException & |
e | ) |
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◆ declarg()
◆ EArg()
TCLAP::ValueArg<size_t> EArg |
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"n" |
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"number-of-events" |
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"Number of events" |
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true |
, |
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10e6 |
, |
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"size_t" |
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) |
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◆ Fill()
Hist_Data Fill |
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range. |
begin(), |
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range. |
end() |
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) |
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◆ Hist_Data()
std::cout<< "Minimum: " << minimum_d << std::endl; std::cout << "-----------------------------------------"<<std::endl; std::cout << "| [Fit] GPU Time (ms) ="<< elapsed_d.count() <<std::endl; std::cout << "-----------------------------------------"<<std::endl; hydra::DenseHistogram<double, 1, hydra::device::sys_t> Hist_Data |
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100 |
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min |
, |
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max |
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) |
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◆ parse()
cmd parse |
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argv |
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argc |
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) |
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◆ SetExtended()
◆ argc
◆ elapsed_d
std::cout<< std::endl<< "Generated data:"<< std::endl; for(size_t i=0; i<10; i++) std::cout << "[" << i << "] :" << data_d[i] << std::endl; auto filter = hydra::wrap_lambda( [=] __hydra_dual__ (double x){ return (x > min) && (x < max ); }); auto range = hydra::filter(data_d, filter); std::cout<< std::endl<< "Filtered data:"<< std::endl; for(size_t i=0; i<10; i++) std::cout << "[" << i << "] :" << range.begin()[i] << std::endl; auto fcn = hydra::make_loglikehood_fcn( model, range); ROOT::Minuit2::MnPrint::SetGlobalLevel(3); hydra::Print::SetLevel(hydra::WARNING); MnStrategy strategy(2); MnMigrad migrad_d(fcn, fcn.GetParameters().GetMnState() , strategy); std::cout<<fcn.GetParameters().GetMnState()<<std::endl; auto start_d = std::chrono::high_resolution_clock::now(); FunctionMinimum minimum_d = FunctionMinimum(migrad_d(50000, 5)); auto end_d = std::chrono::high_resolution_clock::now(); std::chrono::duration<double, std::milli> elapsed_d = end_d - start_d |
◆ Exp_PDF
Initial value:Pdf< FUNCTOR, INTEGRATOR > make_pdf(FUNCTOR const &functor, INTEGRATOR integrator)
Build a hydra::Pdf given a shape described by a functor and a integrator (algorithm or functor)...
Definition: Pdf.h:299
https://en.wikipedia.org/wiki/Exponential_function
Definition: Exponential.h:57
double max
Definition: extended_logLL_fit.inl:121
double min
Definition: extended_logLL_fit.inl:120
Definition: AnalyticalIntegral.inl:39
hydra::Parameter tau_p
Definition: extended_logLL_fit.inl:146
- Examples:
- extended_logLL_fit.inl.
◆ Gauss1_PDF
Initial value:hydra::Parameter mean1_p
Definition: extended_logLL_fit.inl:125
Pdf< FUNCTOR, INTEGRATOR > make_pdf(FUNCTOR const &functor, INTEGRATOR integrator)
Build a hydra::Pdf given a shape described by a functor and a integrator (algorithm or functor)...
Definition: Pdf.h:299
double max
Definition: extended_logLL_fit.inl:121
hydra::Parameter sigma1_p
Definition: extended_logLL_fit.inl:126
Gaussian functions are often used to represent the probability density function of a normally dist...
Definition: Gaussian.h:62
double min
Definition: extended_logLL_fit.inl:120
Definition: AnalyticalIntegral.inl:39
- Examples:
- extended_logLL_fit.inl.
◆ Gauss2_PDF
Initial value:Pdf< FUNCTOR, INTEGRATOR > make_pdf(FUNCTOR const &functor, INTEGRATOR integrator)
Build a hydra::Pdf given a shape described by a functor and a integrator (algorithm or functor)...
Definition: Pdf.h:299
hydra::Parameter sigma2_p
Definition: extended_logLL_fit.inl:136
double max
Definition: extended_logLL_fit.inl:121
Gaussian functions are often used to represent the probability density function of a normally dist...
Definition: Gaussian.h:62
double min
Definition: extended_logLL_fit.inl:120
Definition: AnalyticalIntegral.inl:39
hydra::Parameter mean2_p
Definition: extended_logLL_fit.inl:135
- Examples:
- extended_logLL_fit.inl.
◆ max
◆ mean1_p
◆ mean2_p
◆ min
◆ model
◆ N_Exp_p
◆ N_Gauss_1_p
◆ N_Gauss_2_p
◆ nentries
nentries = EArg.getValue() |
◆ return
◆ sigma1_p
◆ sigma2_p
◆ tau_p
◆ try
Initial value:{
TCLAP::CmdLine cmd("Command line arguments for ", '=')