basic_fit_range_semantics.inl

This example shows how to generate a normal distributed dataset and fit a hydra::Gaussian distribution using hydra's range semantics.

/*----------------------------------------------------------------------------
 *
 *   Copyright (C) 2016 - 2023 Antonio Augusto Alves Junior
 *
 *   This file is part of Hydra Data Analysis Framework.
 *
 *   Hydra is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   Hydra is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with Hydra.  If not, see <http://www.gnu.org/licenses/>.
 *
 *---------------------------------------------------------------------------*/

/*
 * basic_fit_range_semantics.inl
 *
 *  Created on: 01/07/2018
 *      Author: Antonio Augusto Alves Junior
 */

#ifndef BASIC_FIT_RANGE_SEMANTICS_INL_
#define BASIC_FIT_RANGE_SEMANTICS_INL_


/**
 * \example basic_fit_range_semantics.inl
 *
 * This example shows how to generate a normal distributed dataset
 * and fit a hydra::Gaussian distribution using hydra's range semantics.
 *
 */

#include <iostream>
#include <assert.h>
#include <time.h>
#include <chrono>

//command line
#include <tclap/CmdLine.h>

//this lib
#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/Filter.h>
#include <hydra/functions/Gaussian.h>
#include <hydra/DenseHistogram.h>
#include <hydra/RandomFill.h>

//Minuit2
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/MnPrint.h"
#include "Minuit2/MnMigrad.h"
#include "Minuit2/MnMinimize.h"

/*-------------------------------------
 * Include classes from ROOT to fill
 * and draw histograms and plots.
 *-------------------------------------
 */
#ifdef _ROOT_AVAILABLE_

#include <TROOT.h>
#include <TH1D.h>
#include <TApplication.h>
#include <TCanvas.h>

#endif //_ROOT_AVAILABLE_

using namespace ROOT::Minuit2;
using namespace hydra::arguments;

declarg( _X, double)


int main(int argv, char** argc)
{
    size_t nentries = 0;

    try {

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

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

        // Parse the argv array.
        cmd.parse(argv, argc);

        // Get the value parsed by each arg.
        nentries = EArg.getValue();

    }
    catch (TCLAP::ArgException &e)  {
        std::cerr << "error: " << e.error() << " for arg " << e.argId()
                                                        << std::endl;
    }

    //-----------------
    // some definitions
    double min   = -5.0;
    double max   =  5.0;
    double mean  =  0.0;
    double sigma =  1.0;

    //parameters
    hydra::Parameter  mean_p  = hydra::Parameter::Create().Name("Mean").Value(0.5).Error(0.0001).Limits(-1.0, 1.0);
    hydra::Parameter  sigma_p = hydra::Parameter::Create().Name("Sigma").Value(0.5).Error(0.0001).Limits(0.01, 1.5);


    //gaussian function evaluating on argument zero
    hydra::Gaussian< _X> gaussian(mean_p,sigma_p);

    //make model (pdf with analytical integral)
    auto model = hydra::make_pdf(gaussian, hydra::AnalyticalIntegral<hydra::Gaussian< _X>>(min, max) );


    //------------------------
#ifdef _ROOT_AVAILABLE_

    TH1D hist_gaussian_d("gaussian_d", "Gaussian",    100, min, max);
    TH1D hist_fitted_gaussian_d("fitted_gaussian_d", "Gaussian",    100, min, max);

#endif //_ROOT_AVAILABLE_


    //begin raii scope
    {

        //1D device buffer
        hydra::device::vector<double>  data(nentries);

        //-------------------------------------------------------
        //gaussian
        hydra::fill_random(data, gaussian);

        std::cout<< std::endl<< "Generated data:"<< std::endl;
        for(size_t i=0; i<10; i++)
            std::cout << "[" << i << "] :" << data[i] << std::endl;

        //filtering
        auto filter = hydra::wrap_lambda(
                [=] __hydra_dual__ (_X x){
                return (x > min) && (x < max );
        });

        auto range  = hydra::filter(data,  filter);

        std::cout<< std::endl<< "Filtered 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);

        //-------------------------------------------------------
        //fit

        ROOT::Minuit2::MnPrint::SetGlobalLevel(3);
        hydra::Print::SetLevel(hydra::WARNING);

        //minimization strategy
        MnStrategy strategy(2);

        //create Migrad minimizer
        MnMigrad migrad_d(fcn, fcn.GetParameters().GetMnState() ,  strategy);

        //print parameters before fitting
        std::cout<<fcn.GetParameters().GetMnState()<<std::endl;

        //Minimize and profile the time
        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;

        //print minuit result
        std::cout<<"minimum: "<<minimum_d<<std::endl;

        //time
        std::cout << "-----------------------------------------"<<std::endl;
        std::cout << "| 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);
        Hist_Data.Fill( range );


#ifdef _ROOT_AVAILABLE_
        //draw data
        for(size_t i=0;  i<100; i++){
            hist_gaussian_d.SetBinContent(i+1, Hist_Data.GetBinContent(i)  );
        }

        //draw fitted function
        hist_fitted_gaussian_d.Sumw2();
        for (size_t i=0 ; i<=100 ; i++) {
            double x = hist_fitted_gaussian_d.GetBinCenter(i);
            hist_fitted_gaussian_d.SetBinContent(i, fcn.GetPDF()(x) );
        }

        hist_fitted_gaussian_d.Scale(hist_gaussian_d.Integral()/hist_fitted_gaussian_d.Integral() );
#endif //_ROOT_AVAILABLE_

    }//end raii scope


#ifdef _ROOT_AVAILABLE_
    TApplication *myapp=new TApplication("myapp",0,0);

    //draw histograms
    TCanvas canvas_d("canvas_d" ,"Distributions - Device", 500, 500);
    hist_gaussian_d.Draw("hist");
    hist_fitted_gaussian_d.Draw("histsameC");
    hist_fitted_gaussian_d.SetLineColor(2);

    myapp->Run();

#endif //_ROOT_AVAILABLE_

    return 0;
}



#endif /* BASIC_FIT_RANGE_SEMANTICS_INL_ */