A class for correlated Gaussian observables. More...

#include <CorrelatedGaussianObservables.h>

Collaboration diagram for CorrelatedGaussianObservables:

Detailed Description

A class for correlated Gaussian observables.

Author: HEPfit Collaboration

Copyright: GNU General Public License

This class builds the correlated Gaussian observables that are specified in the SomeModel.conf file or specified by the user.

Definition at line 31 of file Observables/src/CorrelatedGaussianObservables.h.

Public Member Functions
void	AddObs (Observable &Obs_i)
	A method to add observables to the list of correlated Gaussian observables. More...

void	ComputeCov (gslpp::matrix< double > Corr)
	Computes the covariance matrix for the correlated Gaussian observables set. More...

virtual double	computeWeight ()
	A method to compute the weight associated with the observable. More...

	CorrelatedGaussianObservables (std::string name_i)
	Constructor. More...

	CorrelatedGaussianObservables ()
	The default Constructor. More...

	CorrelatedGaussianObservables (const CorrelatedGaussianObservables &orig)
	The copy constructor. More...

gslpp::matrix< double >	getCov () const
	A get method to access the covariance matrix of the correlated Gaussian observables. More...

std::string	getName () const
	A get method to access the name of the correlated Gaussian observables set. More...

std::vector< Observable >	getObs () const
	A get method to access the vector of observables that are defined in one correlated Gaussian observables set. More...

Observable	getObs (int i) const
	A get method to access an element of the vector of observables that are defined in one correlated Gaussian observables set. More...

bool	isEOF ()
	A method to check if the end of file has been reached. More...

bool	isPrediction ()
	A method to check if the Correlated Observables are set for prediction. More...

int	ParseCGO (boost::ptr_vector< Observable > &Observables, std::ifstream &ifile, boost::tokenizer< boost::char_separator< char > >::iterator &beg, std::string &infilename, ThObsFactory &myObsFactory, StandardModel *myModel, int lineNo, int rank)
	The parser for CorrelatedGaussianObservables. More...

void	setIsPrediction (bool IsPrediction_i)
	A method to set a set of CGO to be predicted. More...

void	setName (std::string name_i)
	A method to set a set of CGO to be predicted. More...

virtual	~CorrelatedGaussianObservables ()
	The default destructor. More...

Private Attributes
gslpp::matrix< double > *	Cov
	The covariance matrix. More...

std::string	filepath
	The path to the config file being parsed. More...

bool	IsEOF
	A boolean which is true if the end of file is reached. More...

bool	IsPrediction
	Flag to define a set of Correlated Observables to be predicted. More...

std::string	name
	The name of the correlated Gaussian Observables set. More...

std::vector< Observable >	Obs
	A vector of observables whose correlation will be calculated. More...

Constructor & Destructor Documentation

CorrelatedGaussianObservables::CorrelatedGaussianObservables ( std::string name_i )

Constructor.

Parameters

[in] name_i a given name for the set of correlated Gaussian observables

Definition at line 13 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     name = name_i;
     IsPrediction = false;
 }

CorrelatedGaussianObservables::CorrelatedGaussianObservables ( )

The default Constructor.

Definition at line 19 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     name = "";
     IsPrediction = false;
 }

CorrelatedGaussianObservables::CorrelatedGaussianObservables ( const CorrelatedGaussianObservables & orig )

The copy constructor.

Definition at line 25 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     Obs = orig.Obs;
     name = orig.name;
     Cov = new gslpp::matrix<double>(*orig.Cov);
     IsPrediction = orig.IsPrediction;
 }

CorrelatedGaussianObservables::~CorrelatedGaussianObservables ( )

virtual

The default destructor.

Put in to prevent seg fault during error handling in InputParser

Definition at line 33 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {   
     Cov = new gslpp::matrix<double>(10, 10, 0.); 
     delete(Cov);
 }

Member Function Documentation

void CorrelatedGaussianObservables::AddObs ( Observable & Obs_i )

A method to add observables to the list of correlated Gaussian observables.

Parameters

Obs_i reference to an object of type Observable

Definition at line 39 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     Obs.push_back(Obs_i);
 }

void CorrelatedGaussianObservables::ComputeCov ( gslpp::matrix< double > Corr )

Computes the covariance matrix for the correlated Gaussian observables set.

Parameters

Corr	the correlation matrix for the correlated Gassian observables set

Definition at line 44 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     unsigned int size = Obs.size();
     if (Corr.size_i() != size || Corr.size_j() != size)
         throw std::runtime_error("The size of the correlated observables in " + name + " does not match the size of the correlation matrix!");
     Cov = new gslpp::matrix<double>(size, size, 0.);
     for (unsigned int i = 0; i < size; i++)
         for (unsigned int j = 0; j < size; j++)
             (*Cov)(i, j) = Obs.at(i).getErrg() * Corr(i, j) * Obs.at(j).getErrg();
     *Cov = Cov->inverse();    
 }

double CorrelatedGaussianObservables::computeWeight ( )

virtual

A method to compute the weight associated with the observable.

Definition at line 56 of file Observables/src/CorrelatedGaussianObservables.cpp.

 {
     gslpp::vector<double> x(Obs.size());
 
     for (unsigned int i = 0; i < Obs.size(); i++)
         x(i) = Obs.at(i).computeTheoryValue() - Obs.at(i).getAve();
 
     return (-0.5 * x * ((*Cov) * x));
 }

gslpp::matrix<double> CorrelatedGaussianObservables::getCov ( ) const

inline

A get method to access the covariance matrix of the correlated Gaussian observables.

Definition at line 104 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return *Cov;
     }

std::string CorrelatedGaussianObservables::getName ( ) const

inline

A get method to access the name of the correlated Gaussian observables set.

Returns: the name

Definition at line 96 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return name;
     }

std::vector<Observable> CorrelatedGaussianObservables::getObs ( ) const

inline

A get method to access the vector of observables that are defined in one correlated Gaussian observables set.

Returns: a vector of type Observable()

Definition at line 77 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return Obs;
     }

Observable CorrelatedGaussianObservables::getObs ( int i ) const

inline

A get method to access an element of the vector of observables that are defined in one correlated Gaussian observables set.

Returns: an element of the vector of type Observable()

Definition at line 87 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return (Obs.at(i));
     }

bool CorrelatedGaussianObservables::isEOF ( )

inline

A method to check if the end of file has been reached.

Returns: a boolean which is true if the end of file has been reached

Definition at line 132 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return IsEOF;
     }

bool CorrelatedGaussianObservables::isPrediction ( )

inline

A method to check if the Correlated Observables are set for prediction.

Returns: a boolean which is true if the set of CGO are to be predicted

Definition at line 150 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         return IsPrediction;
     }

int CorrelatedGaussianObservables::ParseCGO	(	boost::ptr_vector< Observable > &	Observables,
		std::ifstream &	ifile,
		boost::tokenizer< boost::char_separator< char > >::iterator &	beg,
		std::string &	infilename,
		ThObsFactory &	myObsFactory,
		StandardModel *	myModel,
		int	lineNo,
		int	rank
	)

The parser for CorrelatedGaussianObservables.

Parameters

[in]	Observables	the pointer vector containing the Observables
[in]	ifile	the stream containing the config file to be parsed
[in]	beg	the iterator that parses a line in the config file
[in]	infilename	the name of the config file being parsed
[in]	myModel	a pointer to the model
[in]	lineNo	the current line number at which the file is being parsed
[in]	rank	the rank of the process that is using the parser

Returns: the line number (integer) after the parsing is done

Definition at line 66 of file Observables/src/CorrelatedGaussianObservables.cpp.

                                                       {
     if (infilename.find("\\/") == std::string::npos) filepath = infilename.substr(0, infilename.find_last_of("\\/") + 1);
     boost::char_separator<char> sep(" \t");
     name = *beg;
     ++beg;
     int size = atoi((*beg).c_str());
     
     int nlines = 0;
     std::vector<bool> lines;
     std::string line;
     for (int i = 0; i < size; i++) {
         IsEOF = getline(ifile, line).eof();
         if (line.empty() || line.at(0) == '#') {
             if (rank == 0) throw std::runtime_error("ERROR: no comments or empty lines in CorrelatedGaussianObservables please! In file " + infilename + " at line number:" + boost::lexical_cast<std::string>(lineNo) + ".\n");
             else sleep(2);
         }
         lineNo++;
         boost::tokenizer<boost::char_separator<char> > tok(line, sep);
         beg = tok.begin();
         std::string type = *beg;
         ++beg;
         if (type.compare("Observable") != 0 && type.compare("BinnedObservable") != 0 && type.compare("FunctionObservable") != 0) {
             if (rank == 0) throw std::runtime_error("ERROR: in line no." + boost::lexical_cast<std::string>(lineNo) + " of file " + infilename + ", expecting an Observable or BinnedObservable or FunctionObservable type here...\n");
             else sleep(2);
         }
         Observable * tmpObs = new Observable();
         beg = tmpObs->ParseObservable(type, &tok, beg, filepath, infilename, rank);
         tmpObs->setTho(myObsFactory.CreateThMethod(tmpObs->getThname(), *myModel));
         if (!IsPrediction) {
             if (tmpObs->isTMCMC()) {
                 AddObs(*tmpObs);
                 lines.push_back(true);
                 nlines++;
             } else {
                 Observables.push_back(tmpObs);
                 lines.push_back(false);
             }
         } else {
             if (tmpObs->isTMCMC()) {
                 if (rank == 0) throw std::runtime_error("ERROR: in line no." + boost::lexical_cast<std::string>(lineNo) + " of file " + infilename + ", Observable/BinnedObservable cannot be set to MCMC in CorrelatedObservable. Use CorrelatedGaussianObservable instead.\n");
                 else sleep(2);
             } else {
                 AddObs(*tmpObs);
                 nlines++;
             }
             if (tmpObs->getDistr().compare("weight") == 0 || tmpObs->getDistr().compare("file") == 0) {
                 if (rank == 0) throw std::runtime_error("ERROR: in line no." + boost::lexical_cast<std::string>(lineNo) + " of file " + infilename + ", Observable/BinnedObservable cannot be set to weight or file in CorrelatedObservable. Use CorrelatedGaussianObservable instead.\n");
                 else sleep(2);
             }
         }
     }
 
     if (nlines > 1) {
         if (!IsPrediction) {
             gslpp::matrix<double> myCorr(gslpp::matrix<double>::Id(nlines));
             int ni = 0;
             for (int i = 0; i < size; i++) {
                 IsEOF = getline(ifile, line).eof();
                 if (line.empty() || line.at(0) == '#') {
                     if (rank == 0) throw std::runtime_error("ERROR: no comments or empty lines in CorrelatedGaussianObservables please! In file " + infilename + " at line number:" + boost::lexical_cast<std::string>(lineNo) + ".\n");
                     else sleep(2);
                 }
                 lineNo++;
                 if (lines.at(i)) {
                     boost::tokenizer<boost::char_separator<char> > mytok(line, sep);
                     beg = mytok.begin();
                     int nj = 0;
                     for (int j = 0; j < size; j++) {
                         if ((*beg).compare(0, 1, "0") == 0
                                 || (*beg).compare(0, 1, "1") == 0
                                 || (*beg).compare(0, 1, "-") == 0) {
                             if (std::distance(mytok.begin(), mytok.end()) < size) {
                                 if (rank == 0) throw std::runtime_error(("ERROR: Correlation matrix is of wrong size in Correlated Gaussian Observables: " + name + +" at line number:" + boost::lexical_cast<std::string>(lineNo) + ".\n").c_str());
                                 else sleep(2);
                             }
                             if (lines.at(j)) {
                                 myCorr(ni, nj) = atof((*beg).c_str());
                                 nj++;
                             }
                             beg++;
                         } else {
                             if (rank == 0) throw std::runtime_error("ERROR: invalid correlation matrix for " + name + ". Check element (" + boost::lexical_cast<std::string>(ni + 1) + "," + boost::lexical_cast<std::string>(nj + 1) + ") in line number " + boost::lexical_cast<std::string>(lineNo) + " in file " + infilename + ".\n");
                             else sleep(2);
                         }
                     }
                     ni++;
                 }
             }
             ComputeCov(myCorr);
         } else {
             Cov = new gslpp::matrix<double>(size, size, 0.);
         }
     } else {
         if (rank == 0) std::cout << "\nWARNING: Correlated (Gaussian) Observable " << name.c_str() << " defined with less than two observables" << " in file " << infilename << ". The set is being marked as normal Observables." << std::endl;
         if (getObs().size() == 1) Observables.push_back(new Observable(getObs(0)));
         for (int i = 0; i < size; i++) {
             IsEOF = getline(ifile, line).eof();
             lineNo++;
         }
     }
     return lineNo;
 }

void CorrelatedGaussianObservables::setIsPrediction ( bool IsPrediction_i )

inline

A method to set a set of CGO to be predicted.

Parameters

[in] IsPrediction_i a boolean which is true if the set of CGO is set for prediction

Definition at line 141 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         IsPrediction = IsPrediction_i;
     }

void CorrelatedGaussianObservables::setName ( std::string name_i )

inline

A method to set a set of CGO to be predicted.

Parameters

[in] IsPrediction_i a boolean which is true if the set of CGO is set for prediction

Definition at line 159 of file Observables/src/CorrelatedGaussianObservables.h.

     {
         name = name_i;
     }

Member Data Documentation

gslpp::matrix<double>* CorrelatedGaussianObservables::Cov

private

The covariance matrix.

Definition at line 166 of file Observables/src/CorrelatedGaussianObservables.h.

std::string CorrelatedGaussianObservables::filepath

private

The path to the config file being parsed.

Definition at line 168 of file Observables/src/CorrelatedGaussianObservables.h.

bool CorrelatedGaussianObservables::IsEOF

private

A boolean which is true if the end of file is reached.

Definition at line 169 of file Observables/src/CorrelatedGaussianObservables.h.

bool CorrelatedGaussianObservables::IsPrediction

private

Flag to define a set of Correlated Observables to be predicted.

Definition at line 170 of file Observables/src/CorrelatedGaussianObservables.h.

std::string CorrelatedGaussianObservables::name

private

The name of the correlated Gaussian Observables set.

Definition at line 167 of file Observables/src/CorrelatedGaussianObservables.h.

std::vector<Observable> CorrelatedGaussianObservables::Obs

private

A vector of observables whose correlation will be calculated.

Definition at line 165 of file Observables/src/CorrelatedGaussianObservables.h.

The documentation for this class was generated from the following files:

Detailed Description

Public Member Functions

Private Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation