Minimizer class based on the Gentic algorithm implemented in TMVA

Definition at line 61 of file GeneticMinimizer.h.

Public Member Functions
	GeneticMinimizer (int i=0)

virtual	~GeneticMinimizer ()

virtual void	Clear ()
	reset for consecutive minimizations - implement if needed

virtual double	CovMatrix (unsigned int i, unsigned int j) const
	return covariance matrices element for variables ivar,jvar if the variable is fixed the return value is zero The ordering of the variables is the same as in the parameter and errors vectors

virtual double	Edm () const
	return expected distance reached from the minimum (re-implement if minimizer provides it

virtual const double *	Errors () const
	return errors at the minimum

virtual const double *	MinGradient () const
	return pointer to gradient values at the minimum

virtual bool	Minimize ()
	method to perform the minimization

const GeneticMinimizerParameters &	MinimizerParameters () const

virtual double	MinValue () const
	return minimum function value

virtual unsigned int	NCalls () const
	number of function calls to reach the minimum

virtual unsigned int	NDim () const
	this is <= Function().NDim() which is the total number of variables (free+ constrained ones)

virtual unsigned int	NFree () const
	number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total (re-implement if minimizer supports bounded parameters)

virtual ROOT::Math::MinimizerOptions	Options () const
	retrieve the minimizer options (implement derived class if needed)

virtual bool	ProvidesError () const
	minimizer provides error and error matrix

virtual bool	SetFixedVariable (unsigned int ivar, const std::string &name, double val)
	set a new fixed variable (override if minimizer supports them )

virtual void	SetFunction (const ROOT::Math::IMultiGenFunction &func)
	set the function to minimize

virtual bool	SetLimitedVariable (unsigned int, const std::string &, double, double, double, double)
	set a new upper/lower limited variable (override if minimizer supports them ) otherwise as default set an unlimited variable

virtual void	SetOptions (const ROOT::Math::MinimizerOptions &opt)

void	SetParameters (const GeneticMinimizerParameters &params)

void	SetRandomSeed (int seed)

virtual bool	SetVariable (unsigned int ivar, const std::string &name, double val, double step)
	set a new free variable

virtual const double *	X () const
	return pointer to X values at the minimum

Public Member Functions inherited from ROOT::Math::Minimizer
	Minimizer ()
	Default constructor.

virtual	~Minimizer ()
	Destructor (no operations)

virtual bool	Contour (unsigned int ivar, unsigned int jvar, unsigned int &npoints, double xi, double xj)
	find the contour points (xi, xj) of the function for parameter ivar and jvar around the minimum The contour will be find for value of the function = Min + ErrorUp();

virtual double	Correlation (unsigned int i, unsigned int j) const
	return correlation coefficient between variable i and j.

virtual int	CovMatrixStatus () const
	return status of covariance matrix using Minuit convention {0 not calculated 1 approximated 2 made pos def , 3 accurate} Minimizer who implements covariance matrix calculation will re-implement the method

double	ErrorDef () const
	return the statistical scale used for calculate the error is typically 1 for Chi2 and 0.5 for likelihood minimization

virtual bool	FixVariable (unsigned int ivar)
	fix an existing variable

virtual bool	GetCovMatrix (double *covMat) const
	Fill the passed array with the covariance matrix elements if the variable is fixed or const the value is zero.

virtual bool	GetHessianMatrix (double *hMat) const
	Fill the passed array with the Hessian matrix elements The Hessian matrix is the matrix of the second derivatives and is the inverse of the covariance matrix If the variable is fixed or const the values for that variables are zero.

virtual bool	GetMinosError (unsigned int ivar, double &errLow, double &errUp, int option=0)
	minos error for variable i, return false if Minos failed or not supported and the lower and upper errors are returned in errLow and errUp An extra flag specifies if only the lower (option=-1) or the upper (option=+1) error calculation is run

virtual bool	GetVariableSettings (unsigned int ivar, ROOT::Fit::ParameterSettings &pars) const
	get variable settings in a variable object (like ROOT::Fit::ParamsSettings)

virtual double	GlobalCC (unsigned int ivar) const
	return global correlation coefficient for variable i This is a number between zero and one which gives the correlation between the i-th parameter and that linear combination of all other parameters which is most strongly correlated with i.

virtual bool	Hesse ()
	perform a full calculation of the Hessian matrix for error calculation

virtual bool	IsFixedVariable (unsigned int ivar) const
	query if an existing variable is fixed (i.e.

bool	IsValidError () const
	return true if Minimizer has performed a detailed error validation (e.g. run Hesse for Minuit)

unsigned int	MaxFunctionCalls () const
	max number of function calls

unsigned int	MaxIterations () const
	max iterations

virtual int	MinosStatus () const
	status code of Minos (to be re-implemented by the minimizers supporting Minos)

virtual unsigned int	NIterations () const
	number of iterations to reach the minimum

double	Precision () const
	precision of minimizer in the evaluation of the objective function ( a value <=0 corresponds to the let the minimizer choose its default one)

int	PrintLevel () const
	minimizer configuration parameters

virtual void	PrintResults ()
	return reference to the objective function virtual const ROOT::Math::IGenFunction & Function() const = 0;

virtual bool	ReleaseVariable (unsigned int ivar)
	release an existing variable

virtual bool	Scan (unsigned int ivar, unsigned int &nstep, double x, double y, double xmin=0, double xmax=0)
	scan function minimum for variable i.

void	SetDefaultOptions ()
	reset the defaut options (defined in MinimizerOptions)

void	SetErrorDef (double up)
	set scale for calculating the errors

virtual void	SetFunction (const ROOT::Math::IMultiGradFunction &func)
	set a function to minimize using gradient

virtual bool	SetLowerLimitedVariable (unsigned int ivar, const std::string &name, double val, double step, double lower)
	set a new lower limit variable (override if minimizer supports them )

void	SetMaxFunctionCalls (unsigned int maxfcn)
	set maximum of function calls

void	SetMaxIterations (unsigned int maxiter)
	set maximum iterations (one iteration can have many function calls)

void	SetOptions (const MinimizerOptions &opt)
	set all options in one go

void	SetPrecision (double prec)
	set in the minimizer the objective function evaluation precision ( a value <=0 means the minimizer will choose its optimal value automatically, i.e.

void	SetPrintLevel (int level)
	set print level

void	SetStrategy (int strategyLevel)
	set the strategy

void	SetTolerance (double tol)
	set the tolerance

virtual bool	SetUpperLimitedVariable (unsigned int ivar, const std::string &name, double val, double step, double upper)
	set a new upper limit variable (override if minimizer supports them )

void	SetValidError (bool on)
	flag to check if minimizer needs to perform accurate error analysis (e.g. run Hesse for Minuit)

virtual bool	SetVariableInitialRange (unsigned int, double, double)
	set the initial range of an existing variable

virtual bool	SetVariableLimits (unsigned int ivar, double lower, double upper)
	set the limits of an already existing variable

virtual bool	SetVariableLowerLimit (unsigned int ivar, double lower)
	set the lower-limit of an already existing variable

template<class VariableIterator >
int	SetVariables (const VariableIterator &begin, const VariableIterator &end)
	add variables . Return number of variables successfully added

virtual bool	SetVariableStepSize (unsigned int ivar, double value)
	set the step size of an already existing variable

virtual bool	SetVariableUpperLimit (unsigned int ivar, double upper)
	set the upper-limit of an already existing variable

virtual bool	SetVariableValue (unsigned int ivar, double value)
	set the value of an already existing variable

virtual bool	SetVariableValues (const double *x)
	set the values of all existing variables (array must be dimensioned to the size of the existing parameters)

int	Status () const
	status code of minimizer

int	Strategy () const
	strategy

double	Tolerance () const
	absolute tolerance

virtual int	VariableIndex (const std::string &name) const
	get index of variable given a variable given a name return -1 if variable is not found

virtual std::string	VariableName (unsigned int ivar) const
	get name of variables (override if minimizer support storing of variable names) return an empty string if variable is not found

Protected Member Functions
void	GetGeneticOptions (ROOT::Math::MinimizerOptions &opt) const

Protected Attributes
TMVA::IFitterTarget *	fFitness

double	fMinValue

GeneticMinimizerParameters	fParameters

std::vector< TMVA::Interval * >	fRanges

std::vector< double >	fResult

Protected Attributes inherited from ROOT::Math::Minimizer
MinimizerOptions	fOptions

int	fStatus

bool	fValidError

#include <Math/GeneticMinimizer.h>

Inheritance diagram for ROOT::Math::GeneticMinimizer:

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Constructor & Destructor Documentation

◆ GeneticMinimizer()

ROOT::Math::GeneticMinimizer::GeneticMinimizer ( int i = 0 )

Definition at line 99 of file GeneticMinimizer.cxx.

◆ ~GeneticMinimizer()

ROOT::Math::GeneticMinimizer::~GeneticMinimizer ( )

virtual

Definition at line 118 of file GeneticMinimizer.cxx.

Member Function Documentation

◆ Clear()

void ROOT::Math::GeneticMinimizer::Clear ( )

virtual

reset for consecutive minimizations - implement if needed

Reimplemented from ROOT::Math::Minimizer.

Definition at line 127 of file GeneticMinimizer.cxx.

◆ CovMatrix()

double ROOT::Math::GeneticMinimizer::CovMatrix	(	unsigned int	ivar,
		unsigned int	jvar
	)		const

virtual

return covariance matrices element for variables ivar,jvar if the variable is fixed the return value is zero The ordering of the variables is the same as in the parameter and errors vectors

Reimplemented from ROOT::Math::Minimizer.

Definition at line 369 of file GeneticMinimizer.cxx.

◆ Edm()

double ROOT::Math::GeneticMinimizer::Edm ( ) const

virtual

return expected distance reached from the minimum (re-implement if minimizer provides it

Reimplemented from ROOT::Math::Minimizer.

Definition at line 368 of file GeneticMinimizer.cxx.

◆ Errors()

const double * ROOT::Math::GeneticMinimizer::Errors ( ) const

virtual

return errors at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 367 of file GeneticMinimizer.cxx.

◆ GetGeneticOptions()

void ROOT::Math::GeneticMinimizer::GetGeneticOptions ( ROOT::Math::MinimizerOptions & opt ) const

protected

Definition at line 192 of file GeneticMinimizer.cxx.

◆ MinGradient()

const double * ROOT::Math::GeneticMinimizer::MinGradient ( ) const

virtual

return pointer to gradient values at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 365 of file GeneticMinimizer.cxx.

◆ Minimize()

bool ROOT::Math::GeneticMinimizer::Minimize ( )

virtual

method to perform the minimization

Implements ROOT::Math::Minimizer.

Definition at line 253 of file GeneticMinimizer.cxx.

◆ MinimizerParameters()

const GeneticMinimizerParameters & ROOT::Math::GeneticMinimizer::MinimizerParameters ( ) const

inline

Definition at line 96 of file GeneticMinimizer.h.

◆ MinValue()

double ROOT::Math::GeneticMinimizer::MinValue ( ) const

virtual

return minimum function value

Implements ROOT::Math::Minimizer.

Definition at line 332 of file GeneticMinimizer.cxx.

◆ NCalls()

unsigned int ROOT::Math::GeneticMinimizer::NCalls ( ) const

virtual

number of function calls to reach the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 341 of file GeneticMinimizer.cxx.

◆ NDim()

unsigned int ROOT::Math::GeneticMinimizer::NDim ( ) const

virtual

this is <= Function().NDim() which is the total number of variables (free+ constrained ones)

Implements ROOT::Math::Minimizer.

Definition at line 349 of file GeneticMinimizer.cxx.

◆ NFree()

unsigned int ROOT::Math::GeneticMinimizer::NFree ( ) const

virtual

number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total (re-implement if minimizer supports bounded parameters)

Reimplemented from ROOT::Math::Minimizer.

Definition at line 356 of file GeneticMinimizer.cxx.

◆ Options()

ROOT::Math::MinimizerOptions ROOT::Math::GeneticMinimizer::Options ( ) const

virtual

retrieve the minimizer options (implement derived class if needed)

Reimplemented from ROOT::Math::Minimizer.

Definition at line 186 of file GeneticMinimizer.cxx.

◆ ProvidesError()

bool ROOT::Math::GeneticMinimizer::ProvidesError ( ) const

virtual

minimizer provides error and error matrix

Reimplemented from ROOT::Math::Minimizer.

Definition at line 366 of file GeneticMinimizer.cxx.

◆ SetFixedVariable()

bool ROOT::Math::GeneticMinimizer::SetFixedVariable	(	unsigned int	ivar,
		const std::string &	name,
		double	val
	)

virtual

set a new fixed variable (override if minimizer supports them )

Reimplemented from ROOT::Math::Minimizer.

Definition at line 166 of file GeneticMinimizer.cxx.

◆ SetFunction()

void ROOT::Math::GeneticMinimizer::SetFunction ( const ROOT::Math::IMultiGenFunction & func )

virtual

set the function to minimize

Implements ROOT::Math::Minimizer.

Definition at line 138 of file GeneticMinimizer.cxx.

◆ SetLimitedVariable()

bool ROOT::Math::GeneticMinimizer::SetLimitedVariable	(	unsigned int	ivar,
		const std::string &	name,
		double	val,
		double	step,
		double	lower,
		double	upper
	)

virtual

set a new upper/lower limited variable (override if minimizer supports them ) otherwise as default set an unlimited variable

Reimplemented from ROOT::Math::Minimizer.

Definition at line 147 of file GeneticMinimizer.cxx.

◆ SetOptions()

void ROOT::Math::GeneticMinimizer::SetOptions ( const ROOT::Math::MinimizerOptions & opt )

virtual

Definition at line 218 of file GeneticMinimizer.cxx.

◆ SetParameters()

void ROOT::Math::GeneticMinimizer::SetParameters ( const GeneticMinimizerParameters & params )

Definition at line 178 of file GeneticMinimizer.cxx.

◆ SetRandomSeed()

void ROOT::Math::GeneticMinimizer::SetRandomSeed ( int seed )

inline

Definition at line 94 of file GeneticMinimizer.h.

◆ SetVariable()

bool ROOT::Math::GeneticMinimizer::SetVariable	(	unsigned int	ivar,
		const std::string &	name,
		double	val,
		double	step
	)

virtual

set a new free variable

Implements ROOT::Math::Minimizer.

Definition at line 154 of file GeneticMinimizer.cxx.

◆ X()

const double * ROOT::Math::GeneticMinimizer::X ( ) const

virtual

return pointer to X values at the minimum

Implements ROOT::Math::Minimizer.

Definition at line 337 of file GeneticMinimizer.cxx.

Member Data Documentation

◆ fFitness

TMVA::IFitterTarget* ROOT::Math::GeneticMinimizer::fFitness

protected

Definition at line 107 of file GeneticMinimizer.h.

◆ fMinValue

double ROOT::Math::GeneticMinimizer::fMinValue

protected

Definition at line 108 of file GeneticMinimizer.h.

◆ fParameters

GeneticMinimizerParameters ROOT::Math::GeneticMinimizer::fParameters

protected

Definition at line 111 of file GeneticMinimizer.h.

◆ fRanges

std::vector<TMVA::Interval*> ROOT::Math::GeneticMinimizer::fRanges

protected

Definition at line 106 of file GeneticMinimizer.h.

◆ fResult

std::vector<double> ROOT::Math::GeneticMinimizer::fResult

protected

Definition at line 109 of file GeneticMinimizer.h.

Libraries for ROOT::Math::GeneticMinimizer:

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

math/genetic/inc/Math/GeneticMinimizer.h
math/genetic/src/GeneticMinimizer.cxx

Public Member Functions

Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

◆ GeneticMinimizer()

◆ ~GeneticMinimizer()

Member Function Documentation

◆ Clear()

◆ CovMatrix()

◆ Edm()

◆ Errors()

◆ GetGeneticOptions()

◆ MinGradient()

◆ Minimize()

◆ MinimizerParameters()

◆ MinValue()

◆ NCalls()

◆ NDim()

◆ NFree()

◆ Options()

◆ ProvidesError()

◆ SetFixedVariable()

◆ SetFunction()

◆ SetLimitedVariable()

◆ SetOptions()

◆ SetParameters()

◆ SetRandomSeed()

◆ SetVariable()

◆ X()

Member Data Documentation

◆ fFitness

◆ fMinValue

◆ fParameters

◆ fRanges

◆ fResult