LauSumPdf.cc

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// Copyright University of Warwick 2006 - 2013.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

// Authors:
// Thomas Latham
// John Back
// Paul Harrison

#include <iostream>
#include <vector>

#include "TMath.h"
#include "TSystem.h"

#include "LauConstants.hh"
#include "LauParameter.hh"
#include "LauSumPdf.hh"

ClassImp(LauSumPdf)


LauSumPdf::LauSumPdf(LauAbsPdf* pdf1, LauAbsPdf* pdf2, LauParameter* frac) :
        LauAbsPdf(pdf1 ? pdf1->varNames() : std::vector<TString>(), std::vector<LauAbsRValue*>(), pdf1 ? pdf1->getMinAbscissas() : LauFitData(), pdf1 ? pdf1->getMaxAbscissas() : LauFitData()),
        pdf1_(pdf1),
        pdf2_(pdf2),
        frac_(frac)
{
        // Constructor for the sum PDF.
        // We are defining the sum as:
        // f x (PDF1/S(PDF1)) + (1-f) x (PDF2/S(PDF2))
        // where f is the fraction, x is multiplication, PDFi is the i'th PDF,
        // and S(PDFi) is the integral of the i'th PDF.
        //
        // The last two arguments specify the range in which the PDF is defined, and the PDF
        // will be normalised w.r.t. these limits.

        // So the first thing we have to do is check the pointers are all valid.
        if (!frac || !pdf1 || !pdf2) {
                std::cerr << "ERROR in LauSumPdf constructor : one of the 3 pointers is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Next check that the abscissa ranges are the same for each PDF
        if (pdf1->getMinAbscissa() != pdf2->getMinAbscissa()) {
                std::cerr << "ERROR in LauSumPdf constructor : minimum abscissa values not the same for the two PDFs." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        if (pdf1->getMaxAbscissa() != pdf2->getMaxAbscissa()) {
                std::cerr << "ERROR in LauSumPdf constructor : maximum abscissa values not the same for the two PDFs." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Also check that both PDFs are expecting the same number of input variables
        if (pdf1->nInputVars() != pdf2->nInputVars()) {
                std::cerr << "ERROR in LauSumPdf constructor : number of input variables not the same for the two PDFs." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Also check that both PDFs are expecting the same variable name
        if (pdf1->varNames() != pdf2->varNames()) {
                std::cerr << "ERROR in LauSumPdf constructor : variable name not the same for the two PDFs." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Then we need to grab all the parameters and pass them to the base class.
        // This is so that when we are asked for them they can be put into the fit.
        // The number of parameters is the number in PDF1 + the number in PDF2 + 1 for the fraction.
        UInt_t nPar(pdf1->nParameters()+pdf2->nParameters()+1);
        std::vector<LauAbsRValue*> params;  params.reserve(nPar);
        std::vector<LauAbsRValue*>& pdf1pars = pdf1->getParameters();
        std::vector<LauAbsRValue*>& pdf2pars = pdf2->getParameters();
        params.push_back(frac);
        for (std::vector<LauAbsRValue*>::iterator iter = pdf1pars.begin(); iter != pdf1pars.end(); ++iter) {
                params.push_back(*iter);
        }
        for (std::vector<LauAbsRValue*>::iterator iter = pdf2pars.begin(); iter != pdf2pars.end(); ++iter) {
                params.push_back(*iter);
        }
        this->addParameters(params);

        // Now check that we can find the fraction parameter ok
        frac_ = this->findParameter("frac");
        if (frac_ == 0) {
                std::cerr << "ERROR in LauSumPdf constructor : parameter \"frac\" not found." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Cache the normalisation factor
        this->calcNorm();
}

LauSumPdf::~LauSumPdf() 
{
        // Destructor
}

void LauSumPdf::calcLikelihoodInfo(const LauAbscissas& abscissas)
{
        // Check that the given abscissa is within the allowed range
        if (!this->checkRange(abscissas)) {
                gSystem->Exit(EXIT_FAILURE);
        }

        // Get the up to date parameter values
        Double_t frac = frac_->unblindValue();

        // Evaluate the normalised PDF values
        pdf1_->calcLikelihoodInfo(abscissas);
        pdf2_->calcLikelihoodInfo(abscissas);
        Double_t result1 = pdf1_->getLikelihood();
        Double_t result2 = pdf2_->getLikelihood();

        // Add them together
        Double_t result = frac * result1 + (1.0-frac) * result2;
        this->setUnNormPDFVal(result);
}

void LauSumPdf::calcNorm() 
{
        // Nothing to do here, since it is already normalized
        this->setNorm(1.0);
}

void LauSumPdf::calcPDFHeight( const LauKinematics* kinematics )
{
        if ( this->heightUpToDate() && ! this->isDPDependent() ) {
                return;
        }

        // This method gives you the maximum possible height of the PDF.
        // It combines the maximum heights of the two individual PDFs.
        // So it would give the true maximum if the two individual maxima coincided.
        // It is guaranteed to always be >= the true maximum.

        // Update the heights of the individual PDFs
        pdf1_->calcPDFHeight( kinematics );
        pdf2_->calcPDFHeight( kinematics );

        // Get the up to date parameter values
        Double_t frac = frac_->unblindValue();

        // Find the (un-normalised) individual PDF maxima
        Double_t height1 = pdf1_->getMaxHeight();
        Double_t height2 = pdf2_->getMaxHeight();

        // Get the individual PDF normalisation factors
        Double_t norm1 = pdf1_->getNorm();
        Double_t norm2 = pdf2_->getNorm();

        // Calculate the normalised individual PDF maxima
        height1 /= norm1;
        height2 /= norm2;

        // Combine these heights together
        Double_t height = frac * height1 + (1-frac) * height2;
        this->setMaxHeight(height);
}

// Override the base class methods for cacheInfo and calcLikelihoodInfo(UInt_t iEvt).
// For both of these we delegate to the two constituent PDFs.

void LauSumPdf::cacheInfo(const LauFitDataTree& inputData)
{
        pdf1_->cacheInfo(inputData);
        pdf2_->cacheInfo(inputData);

        // determine whether we are caching our PDF value
        Bool_t doCaching ( this->nFixedParameters() == this->nParameters() );
        this->cachePDF( doCaching );

        // Set whether the height is up-to-date only if it is true for both of the sub-PDFs
        Bool_t hutd = pdf1_->heightUpToDate() && pdf2_->heightUpToDate();
        this->heightUpToDate(hutd);
}

void LauSumPdf::calcLikelihoodInfo(UInt_t iEvt)
{
        // Get the up to date parameter values
        Double_t frac = frac_->unblindValue();

        // Evaluate the normalised PDF values
        pdf1_->calcLikelihoodInfo(iEvt);
        pdf2_->calcLikelihoodInfo(iEvt);
        Double_t result1 = pdf1_->getLikelihood();
        Double_t result2 = pdf2_->getLikelihood();

        // Add them together
        Double_t result = frac * result1 + (1.0-frac) * result2;
        this->setUnNormPDFVal(result);
}