#include <JPoint4DRegressor.hh>

Inheritance diagram for JFIT::JRegressor< JPoint4D, JGandalf >:

Public Types
typedef JGandalf< JPoint4D >	minimiser_type

typedef JRegressor< JPoint4D, JGandalf >	regressor_type

typedef minimiser_type::result_type	result_type

typedef JFIT_LOCAL::JTypedef_t< JModel_t >::parameter_type	parameter_type
	Data type of fit parameter. More...

Public Member Functions
	JRegressor (double sigma)
	Constructor. More...

template<class JHit_t >
result_type	operator() (const JPoint4D &vx, const JHit_t &hit) const

result_type	operator() (const JPoint4D &value, T __begin, T __end)
	Global fit. More...

template<class JFunction_t , class T , class ... Args>
result_type	operator() (const JFunction_t &fit, T __begin, T __end, Args ...args)
	Multi-dimensional fit of multiple data sets. More...

Public Attributes
JLANG::JSharedPointer< JMEstimator >	estimator
	M-Estimator function. More...

double	sigma
	Time resolution [ns]. More...

std::vector< parameter_type >	parameters
	fit parameters More...

int	numberOfIterations
	number of iterations More...

double	lambda
	control parameter More...

JModel_t	value
	value More...

JModel_t	error
	error More...

JMATH::JMatrixNS	V
	Hesse matrix. More...

Static Public Attributes
static int	MAXIMUM_ITERATIONS = 1000
	maximal number of iterations More...

static double	EPSILON = 1.0e-3
	maximal distance to minimum More...

static bool	EPSILON_ABSOLUTE = false
	set epsilon to absolute difference instead of relative More...

static double	LAMBDA_MIN = 0.01
	minimal value control parameter More...

static double	LAMBDA_MAX = 100.0
	maximal value control parameter More...

static double	LAMBDA_UP = 10.0
	multiplication factor control parameter More...

static double	LAMBDA_DOWN = 10.0
	multiplication factor control parameter More...

static double	PIVOT = std::numeric_limits<double>::epsilon()
	minimal value diagonal element of Hesse matrix More...

static int	debug = 0
	debug level (default is off). More...

Private Member Functions
void	reset ()
	Reset current parameters. More...

template<class JFunction_t , class T , class ... Args>
void	update (const JFunction_t &fit, T __begin, T __end, Args ...args)
	Recursive method to update current parameters. More...

template<class JFunction_t >
void	update (const JFunction_t &fit)
	Termination method to update current parameters. More...

Static Private Member Functions
static double	get (const JModel_t &model, double JModel_t::*parameter)
	Read/write access to parameter value by data member. More...

static double &	get (JModel_t &model, double JModel_t::*parameter)
	Read/write access to parameter value by data member. More...

static double	get (const JModel_t &model, const size_t index)
	Read/write access to parameter value by index. More...

static double &	get (JModel_t &model, const size_t index)
	Read/write access to parameter value by index. More...

static double	get (const JModel_t &model, const int index)
	Read/write access to parameter value by index. More...

static double &	get (JModel_t &model, const int index)
	Read/write access to parameter value by index. More...

Private Attributes
std::vector< double >	h

JMATH::JVectorND	x

struct {
result_type result

}	current

struct {
JModel_t value

result_type result

}	previous

Detailed Description

Definition at line 67 of file JPoint4DRegressor.hh.

Member Typedef Documentation

◆ minimiser_type

typedef JGandalf <JPoint4D > JFIT::JAbstractRegressor< JPoint4D , JGandalf >::minimiser_type

inherited

Definition at line 80 of file JRegressor.hh.

◆ regressor_type

typedef JRegressor<JPoint4D , JGandalf > JFIT::JAbstractRegressor< JPoint4D , JGandalf >::regressor_type

inherited

Definition at line 81 of file JRegressor.hh.

◆ result_type

typedef minimiser_type::result_type JFIT::JAbstractRegressor< JPoint4D , JGandalf >::result_type

inherited

Definition at line 82 of file JRegressor.hh.

◆ parameter_type

template<class JModel_t >

typedef JFIT_LOCAL::JTypedef_t<JModel_t>::parameter_type JFIT::JGandalf< JModel_t >::parameter_type

inherited

Data type of fit parameter.

Definition at line 95 of file JGandalf.hh.

Constructor & Destructor Documentation

◆ JRegressor()

JFIT::JRegressor< JPoint4D, JGandalf >::JRegressor ( double sigma )

inline

Constructor.

Definition at line 75 of file JPoint4DRegressor.hh.

     {
       this->sigma = sigma;
     }

Member Function Documentation

◆ operator()() [1/3]

template<class JHit_t >

result_type JFIT::JRegressor< JPoint4D, JGandalf >::operator()	(	const JPoint4D &	vx,
		const JHit_t &	hit
	)		const

inline

Definition at line 89 of file JPoint4DRegressor.hh.

     {
       using namespace JPP;
  
       const double dt = hit.getT() - vx.getT(hit.getPosition());
  
       const double u  = dt / sigma;
       result_type result;
       result.chi2 = estimator->getRho(u) * hit.getW();
  
       JVector3D d = hit.getPosition() - vx.getPosition();
       
       double weight = getIndexOfRefraction() * getInverseSpeedOfLight()/(d.getLength() * sigma);
       
       result.gradient = hit.getW() * JPoint4D( d * weight,
                                                -1/sigma);
       result.gradient.mul(0.5 * estimator->getPsi(u));
       
       return result;
     }

◆ operator()() [2/3]

result_type JFIT::JAbstractRegressor< JPoint4D , JGandalf >::operator()	(	const JPoint4D &	value,
		T	__begin,
		T	__end
	)

inlineinherited

Global fit.

Parameters

value	start value
__begin	begin of data set
__end	end of data set

Returns: chi2

Definition at line 94 of file JRegressor.hh.

     {
       static_cast<minimiser_type&>(*this).value = value;
  
       return static_cast<minimiser_type&>(*this)(static_cast<regressor_type&>(*this), __begin, __end);
     }

◆ operator()() [3/3]

template<class JModel_t >

template<class JFunction_t , class T , class ... Args>

result_type JFIT::JGandalf< JModel_t >::operator()	(	const JFunction_t &	fit,
		T	__begin,
		T	__end,
		Args ...	args
	)

inlineinherited

Multi-dimensional fit of multiple data sets.

The fit function should return the chi2 as well as the partial derivatives for the current value of the model and a given data point.

Parameters

fit	fit function
__begin	begin of data
__end	end of data
args	optional data

Returns: chi2 and gradient

Definition at line 160 of file JGandalf.hh.

     {
       using namespace std;
       using namespace JPP;
  
       // note that all model values should be assigned to the start value of the model before use
       // because the actual list of model parameters can vary from fit to fit
       // (e.g. if model consists of a container).
  
       const size_t N = parameters.size();
  
       V.resize(N);
       h.resize(N);
       x.resize(N);
  
       previous.result.chi2 = numeric_limits<double>::max();
  
       current.result.chi2     = numeric_limits<double>::max();
       current.result.gradient = value;
       current.result.gradient = zero;
  
       error     = value;
       error     = zero;
  
       lambda    = LAMBDA_MIN;
  
       for (numberOfIterations = 0; numberOfIterations != MAXIMUM_ITERATIONS; ++numberOfIterations) {
  
         DEBUG("step:     " << numberOfIterations << endl);
  
         reset();
  
         update(fit, __begin, __end, args...);
  
         DEBUG("lambda:   " << FIXED(12,5) << lambda              << endl);
         DEBUG("chi2:     " << FIXED(12,5) << current.result.chi2 << endl);
  
         if (current.result.chi2 < previous.result.chi2) {
  
           if (numberOfIterations != 0) {
  
             const double tolerance = EPSILON * (EPSILON_ABSOLUTE ? 1.0 : fabs(previous.result.chi2)); 
  
             if (fabs(previous.result.chi2 - current.result.chi2) <= tolerance) {
  
               // normal end
  
               const result_type result = current.result;
  
               reset();
  
               update(fit, __begin, __end, args...);
  
               try {
                 V.invert();
               }
               catch (const exception& error) {}
  
               for (size_t i = 0; i != N; ++i) {
                 get(error, parameters[i]) = sqrt(V(i,i));
               }
  
               return result;
             }
  
             if (lambda > LAMBDA_MIN) {
               lambda /= LAMBDA_DOWN;
             }
           }
  
           // store current values
  
           previous.value  = value;
           previous.result = current.result;
  
         } else {
  
           value   = previous.value;                                 // restore value
  
           lambda *= LAMBDA_UP;
           
           if (lambda > LAMBDA_MAX) {
             break;
           }
  
           reset();
  
           update(fit, __begin, __end, args...);
         }
  
         DEBUG("Hesse matrix:" << endl << V << endl);
  
         // force definite positiveness
  
         for (size_t i = 0; i != N; ++i) {
  
           if (V(i,i) < PIVOT) {
             V(i,i) = PIVOT;
           }
  
           h[i] = 1.0 / sqrt(V(i,i));
         }
  
         // normalisation
         
         for (size_t row = 0; row != N; ++row) {
           for (size_t col = 0; col != row; ++col) {
             V(row,col) *= h[row] * h[col];
             V(col,row)  = V(row,col);
           }
         }
         
         for (size_t i = 0; i != N; ++i) {
           V(i,i) = 1.0 + lambda;
         }
  
         // solve A x = b
  
         for (size_t col = 0; col != N; ++col) {
           x[col] = h[col] * get(current.result.gradient, parameters[col]);
         }
  
         try {
           V.solve(x);
         }
         catch (const exception& error) {
  
           ERROR("JGandalf: " << error.what() << endl << V << endl);
  
           break;
         }
  
         // update value
  
         for (size_t row = 0; row != N; ++row) {
  
           DEBUG("u[" << noshowpos << setw(3) << row << "] = " << showpos << FIXED(15,5) << get(value, parameters[row]));
  
           get(value, parameters[row]) -= h[row] * x[row];
  
           DEBUG(" -> " << FIXED(15,5) << get(value, parameters[row]) << noshowpos << endl);
         }
  
         model(value);
       }
  
       // abnormal end
  
       const result_type result = previous.result;
  
       value   = previous.value;                                     // restore value
  
       reset();
  
       update(fit, __begin, __end, args...);
  
       try {
         V.invert();
       }
       catch (const exception& error) {}
  
       for (size_t i = 0; i != N; ++i) {
         get(error, parameters[i]) = sqrt(V(i,i));
       }
  
       return result;
     }

◆ reset()

template<class JModel_t >

void JFIT::JGandalf< JModel_t >::reset ( )

inlineprivateinherited

Reset current parameters.

Definition at line 349 of file JGandalf.hh.

     {
       using namespace JPP;
  
       current.result.chi2     = 0.0;
       current.result.gradient = zero;
  
       V.reset();          
     }

◆ update() [1/2]

template<class JModel_t >

template<class JFunction_t , class T , class ... Args>

void JFIT::JGandalf< JModel_t >::update	(	const JFunction_t &	fit,
		T	__begin,
		T	__end,
		Args ...	args
	)

inlineprivateinherited

Recursive method to update current parameters.

Parameters

fit	fit function
__begin	begin of data
__end	end of data
args	optional data

Definition at line 369 of file JGandalf.hh.

     {
       for (T i = __begin; i != __end; ++i) {
  
         const result_type& result = fit(value, *i);
  
         current.result.chi2     += result.chi2;
         current.result.gradient += result.gradient;
  
         for (size_t row = 0; row != parameters.size(); ++row) {
           for (size_t col = row; col != parameters.size(); ++col) {
             V(row,col) += get(result.gradient, parameters[row]) * get(result.gradient, parameters[col]);
           }
         }
       }
  
       update(fit, args...);
     }

◆ update() [2/2]

template<class JModel_t >

template<class JFunction_t >

void JFIT::JGandalf< JModel_t >::update ( const JFunction_t & fit )

inlineprivateinherited

Termination method to update current parameters.

Parameters

fit	fit function

Definition at line 395 of file JGandalf.hh.

     {
       for (size_t row = 0; row != parameters.size(); ++row) {
         for (size_t col = 0; col != row; ++col) {
           V(row,col) = V(col,row);
         }
       }
     }

◆ get() [1/6]

template<class JModel_t >

static double JFIT::JGandalf< JModel_t >::get	(	const JModel_t &	model,
		double JModel_t::*	parameter
	)

inlinestaticprivateinherited

Read/write access to parameter value by data member.

Parameters

model	model
parameter	parameter

Returns: value

Definition at line 412 of file JGandalf.hh.

     {
       return model.*parameter;
     }

◆ get() [2/6]

template<class JModel_t >

static double& JFIT::JGandalf< JModel_t >::get	(	JModel_t &	model,
		double JModel_t::*	parameter
	)

inlinestaticprivateinherited

Read/write access to parameter value by data member.

Parameters

model	model
parameter	parameter

Returns: value

Definition at line 425 of file JGandalf.hh.

     {
       return model.*parameter;
     }

◆ get() [3/6]

template<class JModel_t >

static double JFIT::JGandalf< JModel_t >::get	(	const JModel_t &	model,
		const size_t	index
	)

inlinestaticprivateinherited

Read/write access to parameter value by index.

Parameters

model	model
index	index

Returns: value

Definition at line 438 of file JGandalf.hh.

     {
       return model[index];
     }

◆ get() [4/6]

template<class JModel_t >

static double& JFIT::JGandalf< JModel_t >::get	(	JModel_t &	model,
		const size_t	index
	)

inlinestaticprivateinherited

Read/write access to parameter value by index.

Parameters

model	model
index	index

Returns: value

Definition at line 451 of file JGandalf.hh.

     {
       return model[index];
     }

◆ get() [5/6]

template<class JModel_t >

static double JFIT::JGandalf< JModel_t >::get	(	const JModel_t &	model,
		const int	index
	)

inlinestaticprivateinherited

Read/write access to parameter value by index.

Parameters

model	model
index	index

Returns: value

Definition at line 464 of file JGandalf.hh.

     {
       return model[index];
     }

◆ get() [6/6]

template<class JModel_t >

static double& JFIT::JGandalf< JModel_t >::get	(	JModel_t &	model,
		const int	index
	)

inlinestaticprivateinherited

Read/write access to parameter value by index.

Parameters

model	model
index	index

Returns: value

Definition at line 477 of file JGandalf.hh.

     {
       return model[index];
     }

Member Data Documentation

◆ estimator

JLANG::JSharedPointer<JMEstimator> JFIT::JRegressor< JPoint4D, JGandalf >::estimator

M-Estimator function.

Definition at line 110 of file JPoint4DRegressor.hh.

◆ sigma

double JFIT::JRegressor< JPoint4D, JGandalf >::sigma

Time resolution [ns].

Definition at line 111 of file JPoint4DRegressor.hh.

◆ MAXIMUM_ITERATIONS

template<class JModel_t >

int JFIT::JGandalf< JModel_t >::MAXIMUM_ITERATIONS = 1000

staticinherited

maximal number of iterations

maximal number of iterations.

Definition at line 329 of file JGandalf.hh.

◆ EPSILON

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::EPSILON = 1.0e-3

staticinherited

maximal distance to minimum

maximal distance to minimum.

Definition at line 330 of file JGandalf.hh.

◆ EPSILON_ABSOLUTE

template<class JModel_t >

bool JFIT::JGandalf< JModel_t >::EPSILON_ABSOLUTE = false

staticinherited

set epsilon to absolute difference instead of relative

set epsilon to absolute difference instead of relative.

Definition at line 331 of file JGandalf.hh.

◆ LAMBDA_MIN

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::LAMBDA_MIN = 0.01

staticinherited

minimal value control parameter

Definition at line 332 of file JGandalf.hh.

◆ LAMBDA_MAX

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::LAMBDA_MAX = 100.0

staticinherited

maximal value control parameter

Definition at line 333 of file JGandalf.hh.

◆ LAMBDA_UP

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::LAMBDA_UP = 10.0

staticinherited

multiplication factor control parameter

Definition at line 334 of file JGandalf.hh.

◆ LAMBDA_DOWN

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::LAMBDA_DOWN = 10.0

staticinherited

multiplication factor control parameter

Definition at line 335 of file JGandalf.hh.

◆ PIVOT

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::PIVOT = std::numeric_limits<double>::epsilon()

staticinherited

minimal value diagonal element of Hesse matrix

minimal value diagonal element of matrix

Definition at line 336 of file JGandalf.hh.

◆ parameters

template<class JModel_t >

std::vector<parameter_type> JFIT::JGandalf< JModel_t >::parameters

inherited

fit parameters

Definition at line 338 of file JGandalf.hh.

◆ numberOfIterations

template<class JModel_t >

int JFIT::JGandalf< JModel_t >::numberOfIterations

inherited

number of iterations

Definition at line 339 of file JGandalf.hh.

◆ lambda

template<class JModel_t >

double JFIT::JGandalf< JModel_t >::lambda

inherited

control parameter

Definition at line 340 of file JGandalf.hh.

◆ value

template<class JModel_t >

JModel_t JFIT::JGandalf< JModel_t >::value

inherited

value

Definition at line 341 of file JGandalf.hh.

◆ error

template<class JModel_t >

JModel_t JFIT::JGandalf< JModel_t >::error

inherited

error

Definition at line 342 of file JGandalf.hh.

◆ V

template<class JModel_t >

JMATH::JMatrixNS JFIT::JGandalf< JModel_t >::V

inherited

Hesse matrix.

Definition at line 343 of file JGandalf.hh.

◆ h

template<class JModel_t >

std::vector<double> JFIT::JGandalf< JModel_t >::h

privateinherited

Definition at line 482 of file JGandalf.hh.

◆ x

template<class JModel_t >

JMATH::JVectorND JFIT::JGandalf< JModel_t >::x

privateinherited

Definition at line 483 of file JGandalf.hh.

◆ result

template<class JModel_t >

result_type JFIT::JGandalf< JModel_t >::result

inherited

Definition at line 486 of file JGandalf.hh.

◆

struct { ... } JFIT::JGandalf< JModel_t >::current

◆

struct { ... } JFIT::JGandalf< JModel_t >::previous

◆ debug

template<class T >

int JEEP::JMessage< T >::debug = 0

staticinherited

debug level (default is off).

Definition at line 45 of file JMessage.hh.

The documentation for this struct was generated from the following file:

JPoint4DRegressor.hh

Public Types

Public Member Functions

Public Attributes

Static Public Attributes

Private Member Functions

Static Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

◆ minimiser_type

◆ regressor_type

◆ result_type

◆ parameter_type

Constructor & Destructor Documentation

◆ JRegressor()

Member Function Documentation

◆ operator()() [1/3]

◆ operator()() [2/3]

◆ operator()() [3/3]

◆ reset()

◆ update() [1/2]

◆ update() [2/2]

◆ get() [1/6]

◆ get() [2/6]

◆ get() [3/6]

◆ get() [4/6]

◆ get() [5/6]

◆ get() [6/6]

Member Data Documentation

◆ estimator

◆ sigma

◆ MAXIMUM_ITERATIONS

◆ EPSILON

◆ EPSILON_ABSOLUTE

◆ LAMBDA_MIN

◆ LAMBDA_MAX

◆ LAMBDA_UP

◆ LAMBDA_DOWN

◆ PIVOT

◆ parameters

◆ numberOfIterations

◆ lambda

◆ value

◆ error

◆ V

◆ h

◆ x

◆ result

◆

◆

◆ debug