double_gaussian_plus_exponential.inl File Reference

#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/DenseHistogram.h>
#include <hydra/functions/Gaussian.h>
#include <hydra/functions/Exponential.h>
#include <hydra/Placeholders.h>
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/MnPrint.h"
#include "Minuit2/MnMigrad.h"
#include "Minuit2/MnMinimize.h"

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Macros
#define	DOUBLE_GAUSSIAN_PLUS_EXPONENTIAL_INL_

Functions
cmd	add (EArg)
	catch (TCLAP::ArgException &e)
	declarg (_X, double) int main(int argv
TCLAP::ValueArg< size_t >	EArg ("n", "number-of-events","Number of events", true, 10e6, "size_t")
Hist_Data	Fill (range.begin(), range.end())
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)
cmd	parse (argv, argc)
model	SetExtended (1)

Variables
char **	argc
auto	Background_PDF = hydra::make_pdf(hydra::Exponential<_X>(tau) , hydra::AnalyticalIntegral<hydra::Exponential<_X>>(min, max))
auto	Core_PDF = hydra::make_pdf( hydra::Gaussian<_X>(mean, sigma_core), hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))
std::cout<< std::endl<< "Generated data:"<< std::endl;for(size_t i=0;i< 10;i++) std::cout<< "["<< i<< "] :"<< range[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(5000, 5));auto end_d=std::chrono::high_resolution_clock::now();std::chrono::duration< double, std::milli >	elapsed_d = end_d - start_d
auto	fraction = hydra::Parameter("Core_Fraction" , 0.5, 0.001, 0.2 , 0.7)
double	max = 10.0
auto	mean = hydra::Parameter::Create().Name("Mean").Value(0.0).Error(0.0001).Limits(-1.5, 1.5)
double	min = -10.0
auto	model = hydra::add_pdfs( {N_Signal, N_Background}, Signal_PDF, Background_PDF)
char const *	model_name = "Gaussian (core) + Gaussian (tail) + Exponential"
hydra::Parameter	N_Background ("N_Background", 2000, 1, 100, nentries)
hydra::Parameter	N_Signal ("N_Signal", 2000, 1, 100, nentries)
	nentries = EArg.getValue()
auto	range = hydra::sample(data, min, max, model.GetFunctor())
	return
auto	sigma_core = hydra::Parameter::Create().Name("Sigma_Core").Value(0.5).Error(0.0001).Limits(0.1, 1.0)
auto	sigma_tail = hydra::Parameter::Create().Name("Sigma_Tail").Value(1.5).Error(0.0001).Limits(1.0, 2.0)
auto	Signal_PDF = hydra::add_pdfs( std::array<hydra::Parameter, 1>{fraction}, Core_PDF, Tail_PDF)
auto	Tail_PDF = hydra::make_pdf( hydra::Gaussian<_X>(mean, sigma_tail), hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))
auto	tau = hydra::Parameter::Create().Name("Tau").Value(-0.5).Error(0.0001).Limits(-1.0, 0.0)
	try

Macro Definition Documentation

DOUBLE_GAUSSIAN_PLUS_EXPONENTIAL_INL_

#define DOUBLE_GAUSSIAN_PLUS_EXPONENTIAL_INL_

Function Documentation

add()

cmd add

(

EArg

)

catch()

catch

(

TCLAP::ArgException &

e

)

declarg()

declarg	(	_X	,
		double
	)

Examples:

double_gaussian_plus_exponential.inl.

EArg()

TCLAP::ValueArg<size_t> EArg	(	"n"	,
		"number-of-events"	,
		"Number of events"	,
		true	,
		10e6	,
		"size_t"
	)

Examples:

double_gaussian_plus_exponential.inl.

Fill()

Hist_Data Fill	(	range.	begin(),
		range.	end()
	)

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	(	100	,
		min	,
		max
	)

Examples:

double_gaussian_plus_exponential.inl.

parse()

cmd parse	(	argv	,
		argc
	)

SetExtended()

model SetExtended

(

1

)

Variable Documentation

argc

char** argc

Initial value:

{
    size_t nentries = 0

Examples:

double_gaussian_plus_exponential.inl.

Background_PDF

auto Background_PDF = hydra::make_pdf(hydra::Exponential<_X>(tau) , hydra::AnalyticalIntegral<hydra::Exponential<_X>>(min, max))

Examples:

double_gaussian_plus_exponential.inl.

Core_PDF

auto Core_PDF = hydra::make_pdf( hydra::Gaussian<_X>(mean, sigma_core), hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))

Examples:

double_gaussian_plus_exponential.inl.

elapsed_d

std::cout<< std::endl<< "Generated data:"<< std::endl; for(size_t i=0; i< 10; i++) std::cout << "[" << i << "] :" << range[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(5000, 5)); auto end_d = std::chrono::high_resolution_clock::now(); std::chrono::duration<double, std::milli> elapsed_d = end_d - start_d

Examples:

double_gaussian_plus_exponential.inl.

fraction

auto fraction = hydra::Parameter("Core_Fraction" , 0.5, 0.001, 0.2 , 0.7)

Examples:

double_gaussian_plus_exponential.inl.

max

double max = 10.0

Examples:

double_gaussian_plus_exponential.inl.

mean

auto mean = hydra::Parameter::Create().Name("Mean").Value(0.0).Error(0.0001).Limits(-1.5, 1.5)

Examples:

double_gaussian_plus_exponential.inl.

min

double min = -10.0

Examples:

double_gaussian_plus_exponential.inl.

model

auto model = hydra::add_pdfs( {N_Signal, N_Background}, Signal_PDF, Background_PDF)

Examples:

double_gaussian_plus_exponential.inl.

model_name

char const* model_name = "Gaussian (core) + Gaussian (tail) + Exponential"

Examples:

double_gaussian_plus_exponential.inl.

N_Background

hydra::Parameter N_Background("N_Background", 2000, 1, 100, nentries)

Examples:

double_gaussian_plus_exponential.inl.

N_Signal

hydra::Parameter N_Signal("N_Signal",2000, 1, 100, nentries)

Examples:

double_gaussian_plus_exponential.inl.

nentries

nentries = EArg.getValue()

Examples:

double_gaussian_plus_exponential.inl.

range

auto range = hydra::sample(data, min, max, model.GetFunctor())

Examples:

double_gaussian_plus_exponential.inl.

return

return

sigma_core

auto sigma_core = hydra::Parameter::Create().Name("Sigma_Core").Value(0.5).Error(0.0001).Limits(0.1, 1.0)

Examples:

double_gaussian_plus_exponential.inl.

sigma_tail

auto sigma_tail = hydra::Parameter::Create().Name("Sigma_Tail").Value(1.5).Error(0.0001).Limits(1.0, 2.0)

Examples:

double_gaussian_plus_exponential.inl.

Signal_PDF

auto Signal_PDF = hydra::add_pdfs( std::array<hydra::Parameter, 1>{fraction}, Core_PDF, Tail_PDF)

Examples:

double_gaussian_plus_exponential.inl.

Tail_PDF

auto Tail_PDF = hydra::make_pdf( hydra::Gaussian<_X>(mean, sigma_tail), hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))

Examples:

double_gaussian_plus_exponential.inl.

tau

auto tau = hydra::Parameter::Create().Name("Tau").Value(-0.5).Error(0.0001).Limits(-1.0, 0.0)

Examples:

double_gaussian_plus_exponential.inl.

try

try

Initial value:

{

        TCLAP::CmdLine cmd("Command line arguments for ", '=')