fractional_logLL_fit.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/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"
#include "Minuit2/MnMinos.h"
#include "Minuit2/MnContours.h"
#include "Minuit2/CombinedMinimizer.h"
#include "Minuit2/MnPlot.h"
#include "Minuit2/MinosError.h"
#include "Minuit2/ContoursError.h"
#include "Minuit2/VariableMetricMinimizer.h"

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Macros
#define	FRACTIONAL_LOGLL_FIT_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)

Variables
char **	argc
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.begin(), range.end());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(std::numeric_limits< unsigned int >::max(), 5));auto end_d=std::chrono::high_resolution_clock::now();std::chrono::duration< double, std::milli >	elapsed_d = end_d - start_d
auto	Exp_PDF
hydra::Parameter	F_Gauss_1_p ("F_Gauss1", 0.5, 0.001, 0.1, 0.5)
hydra::Parameter	F_Gauss_2_p ("F_Gauss2", 0.5, 0.001, 0.1, 0.5)
auto	Gauss1_PDF
auto	Gauss2_PDF
double	max = 10.0
hydra::Parameter	mean1_p = hydra::Parameter::Create().Name("Mean_1").Value( 2.5) .Error(0.0001).Limits(0.0, 10.0)
hydra::Parameter	mean2_p = hydra::Parameter::Create().Name("Mean_2").Value(5.0) .Error(0.0001).Limits(0.0, 10.0)
double	min = 0.0
auto	model = hydra::add_pdfs( std::array<hydra::Parameter,2>{F_Gauss_1_p, F_Gauss_2_p }, Gauss1_PDF, Gauss2_PDF, Exp_PDF)
	nentries = EArg.getValue()
	return
hydra::Parameter	sigma1_p = hydra::Parameter::Create().Name("Sigma_1").Value(0.5).Error(0.0001).Limits(0.01, 1.5)
hydra::Parameter	sigma2_p = hydra::Parameter::Create().Name("Sigma_2").Value(0.5).Error(0.0001).Limits(0.01, 1.5)
hydra::Parameter	tau_p = hydra::Parameter::Create().Name("Tau").Value(1.0).Error(0.0001).Limits(-2.0, 2.0)
	try

Macro Definition Documentation

FRACTIONAL_LOGLL_FIT_INL_

#define FRACTIONAL_LOGLL_FIT_INL_

Function Documentation

add()

cmd add

(

EArg

)

catch()

catch

(

TCLAP::ArgException &

e

)

declarg()

declarg	(	_X	,
		double
	)

Examples:

fractional_logLL_fit.inl.

EArg()

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

Examples:

fractional_logLL_fit.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:

fractional_logLL_fit.inl.

parse()

cmd parse	(	argv	,
		argc
	)

Variable Documentation

argc

char** argc

Initial value:

{
    size_t nentries = 0

Examples:

fractional_logLL_fit.inl.

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.begin(), range.end() ); 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(std::numeric_limits<unsigned int>::max(), 5)); auto end_d = std::chrono::high_resolution_clock::now(); std::chrono::duration<double, std::milli> elapsed_d = end_d - start_d

Examples:

fractional_logLL_fit.inl.

Exp_PDF

auto Exp_PDF

Initial value:

= hydra::make_pdf( hydra::Exponential<_X>(tau_p),
            hydra::AnalyticalIntegral<hydra::Exponential<_X>>(min, max))

Examples:

fractional_logLL_fit.inl.

F_Gauss_1_p

hydra::Parameter F_Gauss_1_p("F_Gauss1",0.5, 0.001, 0.1, 0.5)

Examples:

fractional_logLL_fit.inl.

F_Gauss_2_p

hydra::Parameter F_Gauss_2_p("F_Gauss2",0.5, 0.001, 0.1, 0.5)

Examples:

fractional_logLL_fit.inl.

Gauss1_PDF

auto Gauss1_PDF

Initial value:

= hydra::make_pdf( hydra::Gaussian<_X>(mean1_p, sigma1_p),
            hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))

Examples:

fractional_logLL_fit.inl.

Gauss2_PDF

auto Gauss2_PDF

Initial value:

= hydra::make_pdf(hydra::Gaussian<_X>(mean2_p, sigma2_p),
            hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max))

Examples:

fractional_logLL_fit.inl.

max

double max = 10.0

Examples:

fractional_logLL_fit.inl.

mean1_p

hydra::Parameter mean1_p = hydra::Parameter::Create().Name("Mean_1").Value( 2.5) .Error(0.0001).Limits(0.0, 10.0)

Examples:

fractional_logLL_fit.inl.

mean2_p

hydra::Parameter mean2_p = hydra::Parameter::Create().Name("Mean_2").Value(5.0) .Error(0.0001).Limits(0.0, 10.0)

Examples:

fractional_logLL_fit.inl.

min

double min = 0.0

Examples:

fractional_logLL_fit.inl.

model

auto model = hydra::add_pdfs( std::array<hydra::Parameter,2>{F_Gauss_1_p, F_Gauss_2_p }, Gauss1_PDF, Gauss2_PDF, Exp_PDF)

Examples:

fractional_logLL_fit.inl.

nentries

nentries = EArg.getValue()

Examples:

fractional_logLL_fit.inl.

return

return

sigma1_p

hydra::Parameter sigma1_p = hydra::Parameter::Create().Name("Sigma_1").Value(0.5).Error(0.0001).Limits(0.01, 1.5)

Examples:

fractional_logLL_fit.inl.

sigma2_p

hydra::Parameter sigma2_p = hydra::Parameter::Create().Name("Sigma_2").Value(0.5).Error(0.0001).Limits(0.01, 1.5)

Examples:

fractional_logLL_fit.inl.

tau_p

hydra::Parameter tau_p = hydra::Parameter::Create().Name("Tau").Value(1.0).Error(0.0001).Limits(-2.0, 2.0)

Examples:

fractional_logLL_fit.inl.

try

try

Initial value:

{

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