LauSigmoidPdf.cc

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/*
Copyright 2012 University of Warwick
 
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
 
    http://www.apache.org/licenses/LICENSE-2.0
 
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
 
/*
Laura++ package authors:
John Back
Paul Harrison
Thomas Latham
*/
 
#include <iostream>
#include <vector>
using std::cerr;
using std::cout;
using std::endl;
 
#include "LauConstants.hh"
#include "LauSigmoidPdf.hh"
 
#include "TMath.h"
#include "TSystem.h"
 
LauSigmoidPdf::LauSigmoidPdf( const TString& theVarName,
                              const std::vector<LauAbsRValue*>& params,
                              Double_t minAbscissa,
                              Double_t maxAbscissa ) :
    LauAbsPdf( theVarName, params, minAbscissa, maxAbscissa ),
    a_( 0 ),
    b_( 0 )
{
    // Constructor for the general form of a Sigmoid PDF.
    //
    // The parameters in params "a" and "b" define the steepness of the
    // slope and shift of the distribution (negative parameter "a" flips
    // the distribution around the y-axis).
    // The last two arguments specify the range in which the PDF is
    // defined, and the PDF will be normalised w.r.t. these limits.
 
    a_ = this->findParameter( "a" );
    b_ = this->findParameter( "b" );
 
    if ( ( this->nParameters() != 2 ) || ( a_ == 0 ) || ( b_ == 0 ) ) {
        cerr << "ERROR in LauSigmoidPdf constructor: LauSigmoidPdf requires 2 parameters: \"a\" and \"b\"."
             << endl;
        gSystem->Exit( EXIT_FAILURE );
    }
 
    // Cache the normalisation factor
    this->calcNorm();
}
 
LauSigmoidPdf::~LauSigmoidPdf()
{
    // Destructor
}
 
LauSigmoidPdf::LauSigmoidPdf( const LauSigmoidPdf& other ) :
    LauAbsPdf( other.varName(), other.getParameters(), other.getMinAbscissa(), other.getMaxAbscissa() )
{
    // Copy constructor
    this->setRandomFun( other.getRandomFun() );
    this->calcNorm();
}
 
void LauSigmoidPdf::calcLikelihoodInfo( const LauAbscissas& abscissas )
{
    // Check that the given abscissa is within the allowed range
    if ( ! this->checkRange( abscissas ) ) {
        gSystem->Exit( EXIT_FAILURE );
    }
 
    // Get our abscissa
    Double_t abscissa = abscissas[0];
 
    // Get the up to date parameter values
    Double_t a = a_->unblindValue();
    Double_t b = b_->unblindValue();
 
    // Calculate the value of the exponent for the given value of the abscissa.
 
    Double_t exponent = -a * abscissa + b;
 
    Double_t value = 1.0 / ( 1.0 + TMath::Exp( exponent ) );
 
    this->setUnNormPDFVal( value );
 
    // if the parameters are floating then we
    // need to recalculate the normalisation
    if ( ! this->cachePDF() && ! this->withinNormCalc() && ! this->withinGeneration() ) {
        this->calcNorm();
    }
}
 
void LauSigmoidPdf::calcNorm()
{
    // Get the up to date parameter values
    Double_t a = a_->unblindValue();
    Double_t b = b_->unblindValue();
 
    // Calculate the normalisation of the sigmoid and cache it.
    Double_t norm( 0.0 );
 
    if ( TMath::Abs( a ) > 1e-10 ) {
        Double_t expb = TMath::Exp( b );
 
        norm = TMath::Log( TMath::Exp( a * this->getMaxAbscissa() ) + expb ) -
               TMath::Log( TMath::Exp( a * this->getMinAbscissa() ) + expb );
        norm /= a;
    }
 
    this->setNorm( norm );
}
 
void LauSigmoidPdf::calcPDFHeight( const LauKinematics* /*kinematics*/ )
{
    if ( this->heightUpToDate() ) {
        return;
    }
 
    // Get the up to date parameter values
    Double_t a = a_->unblindValue();
 
    LauAbscissas maxPoint( 1 );
    maxPoint[0] = 0;
 
    // Calculate the PDF height for the Sigmoid function.
    if ( a > 0.0 ) {
        maxPoint[0] = this->getMaxAbscissa();
    } else {
        maxPoint[0] = this->getMinAbscissa();
    }
    this->calcLikelihoodInfo( maxPoint );
 
    Double_t height = this->getUnNormLikelihood();
    this->setMaxHeight( height );
}