multidimensional_fit.inl File Reference

#include <iostream>
#include <assert.h>
#include <time.h>
#include <chrono>
#include <random>
#include <algorithm>
#include <future>
#include <tclap/CmdLine.h>
#include <hydra/device/System.h>
#include <hydra/host/System.h>
#include <hydra/Function.h>
#include <hydra/Lambda.h>
#include <hydra/Random.h>
#include <hydra/Algorithm.h>
#include <hydra/Tuple.h>
#include <hydra/Distance.h>
#include <hydra/multiarray.h>
#include <hydra/multivector.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/Plain.h>
#include <hydra/DenseHistogram.h>
#include <hydra/functions/Gaussian.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	MULTIDIMENSIONAL_FIT_INL_

Functions
cmd	add (EArg)
	catch (TCLAP::ArgException &e)
	declarg (_X, double) declarg(_Y
double	declarg (_Z, 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<< "| GPU Time (ms) ="<< elapsed_d.count()<< std::endl;std::cout<< "-----------------------------------------"<< std::endl;hydra::DenseHistogram< double, 3, hydra::device::sys_t >	Hist_Data ({100, 100, 100}, {min, min, min}, {max, max, max })
cmd	parse (argv, argc)
GaussianX_Handler	wait ()

Variables
double 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__(_X x, _Y y, _Z z){ bool decision=((x > min) &&(x< max))&&((y > min) &&(y< max))&&((z > min) &&(z< max));return decision;});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);fcn.GetPDF().PrintRegisteredParameters();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	gaussian
auto	GaussianX_Handler
auto	GaussianY_Handler
auto	GaussianZ_Handler
hydra::Plain< 3, hydra::device::sys_t >	Integrator ({min, min, min},{ max, max, max }, 50000)
double	max = 6.0
double	meanx = 0.0
hydra::Parameter	meanx_p
double	meany = 0.0
hydra::Parameter	meany_p
double	meanz = 0.0
hydra::Parameter	meanz_p
double	min = -6.0
auto	model = hydra::make_pdf(gaussian, Integrator)
	nentries = EArg.getValue()
	return
double	sigmax = 0.5
hydra::Parameter	sigmax_p
double	sigmay = 1.5
hydra::Parameter	sigmay_p
double	sigmaz = 1.0
hydra::Parameter	sigmaz_p
	try

Macro Definition Documentation

MULTIDIMENSIONAL_FIT_INL_

#define MULTIDIMENSIONAL_FIT_INL_

Function Documentation

add()

cmd add

(

EArg

)

catch()

catch

(

TCLAP::ArgException &

e

)

declarg() [1/2]

declarg	(	_X	,
		double
	)

Examples:

multidimensional_fit.inl.

declarg() [2/2]

double declarg	(	_Z	,
		double
	)

EArg()

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

Examples:

multidimensional_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 << "| GPU Time (ms) =" << elapsed_d.count() << std::endl; std::cout << "-----------------------------------------" << std::endl; hydra::DenseHistogram<double, 3, hydra::device::sys_t> Hist_Data	(	{100, 100, 100}	,
		{min, min, min}	,
		{max, max, max }
	)

Examples:

multidimensional_fit.inl.

parse()

cmd parse	(	argv	,
		argc
	)

wait()

GaussianZ_Handler wait

(

)

Variable Documentation

argc

double char** argc

Initial value:

{
    size_t nentries = 0

Examples:

multidimensional_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__ ( _X x, _Y y, _Z z ){ bool decision = ((x > min) && (x < max ))&& ((y > min) && (y < max ))&& ((z > min) && (z < max )); return decision; }); 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); fcn.GetPDF().PrintRegisteredParameters(); 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:

multidimensional_fit.inl.

gaussian

auto gaussian

Initial value:

= hydra::wrap_lambda(
        [=] __hydra_dual__  (unsigned int npar, const hydra::Parameter* params, _X x, _Y y, _Z z ){

        double g = 1.0;

        double mean[3]  = {params[0].GetValue(), params[2].GetValue(), params[4].GetValue()};
        double sigma[3] = {params[1].GetValue(), params[3].GetValue(), params[5].GetValue()};
        double X[3]     = {x, y, z};

        for(size_t i=0; i<3; i++) {

            double m2 = (X[i] - mean[i] )*(X[i] - mean[i] );
            double s2 = sigma[i]*sigma[i];

            g *= exp(-m2/(2.0 * s2 ))/( sqrt(2.0*s2*PI));
        }

        return g;

    }, meanx_p, sigmax_p, meany_p, sigmay_p, meanz_p, sigmaz_p)

Examples:

multidimensional_fit.inl.

GaussianX_Handler

auto GaussianX_Handler

Initial value:

= std::async(std::launch::async,
                [&data_d, meanx, sigmax]( ) {

            hydra::fill_random(data_d.begin<_X>(), data_d.end<_X>(), hydra::Gaussian<_X>(meanx, sigmax), 159 );
        })

Examples:

multidimensional_fit.inl.

GaussianY_Handler

auto GaussianY_Handler

Initial value:

= std::async(std::launch::async,
                [&data_d, meany, sigmay]( ) {

            hydra::fill_random(data_d.begin<_Y>(), data_d.end<_Y>(), hydra::Gaussian<_Y>(meany, sigmay), 753 );
        })

Examples:

multidimensional_fit.inl.

GaussianZ_Handler

auto GaussianZ_Handler

Initial value:

= std::async(std::launch::async,
                [&data_d, meanz, sigmaz]( ) {

            hydra::fill_random(data_d.begin<_Z>(), data_d.end<_Z>(), hydra::Gaussian<_Z>(meanz, sigmaz), 789 );
        })

Examples:

multidimensional_fit.inl.

Integrator

hydra::Plain<3, hydra::device::sys_t> Integrator({min, min, min},{ max, max, max }, 50000)

Examples:

multidimensional_fit.inl.

max

double max = 6.0

Examples:

multidimensional_fit.inl.

meanx

double meanx = 0.0

Examples:

multidimensional_fit.inl.

meanx_p

hydra::Parameter meanx_p

Initial value:

=
            hydra::Parameter::Create().Name("Mean_X").Value(0.0).Error(0.0001).Limits(
                    -1.0, 1.0)

Examples:

multidimensional_fit.inl.

meany

double meany = 0.0

Examples:

multidimensional_fit.inl.

meany_p

hydra::Parameter meany_p

Initial value:

=
            hydra::Parameter::Create().Name("Mean_Y").Value(0.0).Error(0.0001).Limits(-1.0, 1.0)

Examples:

multidimensional_fit.inl.

meanz

double meanz = 0.0

Examples:

multidimensional_fit.inl.

meanz_p

hydra::Parameter meanz_p

Initial value:

=
            hydra::Parameter::Create().Name("Mean_Z").Value(0.0).Error(0.0001).Limits(-1.0, 1.0)

Examples:

multidimensional_fit.inl.

min

double min = -6.0

Examples:

multidimensional_fit.inl.

model

auto model = hydra::make_pdf(gaussian, Integrator)

Examples:

multidimensional_fit.inl.

nentries

nentries = EArg.getValue()

Examples:

multidimensional_fit.inl.

return

return

sigmax

double sigmax = 0.5

Examples:

multidimensional_fit.inl, and sample_distribution.inl.

sigmax_p

hydra::Parameter sigmax_p

Initial value:

=
            hydra::Parameter::Create().Name("Sigma_X").Value(2.0).Error(0.0001).Limits(0.1, 3.0)

Examples:

multidimensional_fit.inl.

sigmay

double sigmay = 1.5

Examples:

multidimensional_fit.inl, and sample_distribution.inl.

sigmay_p

hydra::Parameter sigmay_p

Initial value:

=
            hydra::Parameter::Create().Name("Sigma_Y").Value(2.0).Error(0.01).Limits(0.1, 3.0)

Examples:

multidimensional_fit.inl.

sigmaz

double sigmaz = 1.0

Examples:

multidimensional_fit.inl, and sample_distribution.inl.

sigmaz_p

hydra::Parameter sigmaz_p

Initial value:

=
            hydra::Parameter::Create().Name("Sigma_Z").Value(2.0).Error(0.0001).Limits(0.1, 3.0)

Examples:

multidimensional_fit.inl.

try

try

Initial value:

{

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