Implementation of the standard $\chi^2$ metric derived from maximum likelihood with Gaussian uncertainties. More...

Inheritance diagram for Chi2Metric:

Collaboration diagram for Chi2Metric:

Public Member Functions
	Chi2Metric ()

Chi2Metric *	clone () const override

virtual double	compute (const SimDataPair &data_pair, bool use_weights) const
	Computes metric value from SimDataPair object. More...

double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > uncertainties, std::vector< double > weight_factors) const override
	Computes metric value from data arrays. More...

double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > weight_factors) const override
	Computes metric value from data arrays. More...

auto	norm () const
	Returns a copy of the normalization function used. More...

void	setNorm (std::function< double(double)> norm)

virtual void	transferToCPP ()
	Used for Python overriding of clone (see swig/tweaks.py) More...

Private Attributes
std::function< double(double)>	m_norm

Detailed Description

Implementation of the standard $\chi^2$ metric derived from maximum likelihood with Gaussian uncertainties.

With default L2 norm corresponds to the formula

$\chi^2 = \sum \frac{(I - D)^2}{\delta_D^2}$

Definition at line 77 of file ObjectiveMetric.h.

Constructor & Destructor Documentation

◆ Chi2Metric()

Chi2Metric::Chi2Metric ( )

Definition at line 81 of file ObjectiveMetric.cpp.

81 : ObjectiveMetric(ObjectiveMetricUtils::l2Norm()) {}

ObjectiveMetric::ObjectiveMetric

ObjectiveMetric(std::function< double(double)> norm)

Definition: ObjectiveMetric.cpp:58

ObjectiveMetricUtils::l2Norm

const std::function< double(double)> l2Norm()

Returns L2 normalization function.

Definition: ObjectiveMetricUtils.cpp:53

Member Function Documentation

◆ clone()

Chi2Metric * Chi2Metric::clone ( ) const

overridevirtual

Implements ObjectiveMetric.

Reimplemented in RQ4Metric, RelativeDifferenceMetric, and PoissonLikeMetric.

Definition at line 83 of file ObjectiveMetric.cpp.

 {
     return copyMetric(*this);
 }

◆ compute()

double ObjectiveMetric::compute	(	const SimDataPair &	data_pair,
		bool	use_weights
	)		const

virtualinherited

Computes metric value from SimDataPair object.

Calls computeFromArrays internally.

Parameters

data_pair	SimDataPair object. Can optionally contain data uncertainties
use_weights	boolean, defines if data uncertainties should be taken into account

Reimplemented in RQ4Metric.

Definition at line 60 of file ObjectiveMetric.cpp.

 {
     if (use_weights && !data_pair.containsUncertainties())
         throw std::runtime_error("Error in ObjectiveMetric::compute: the metric is weighted, but "
                                  "the simulation-data pair does not contain uncertainties");
  
     if (use_weights)
         return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
                                  data_pair.uncertainties_array(), data_pair.user_weights_array());
     else
         return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
                                  data_pair.user_weights_array());
 }

References ObjectiveMetric::computeFromArrays(), SimDataPair::containsUncertainties(), SimDataPair::experimental_array(), SimDataPair::simulation_array(), SimDataPair::uncertainties_array(), and SimDataPair::user_weights_array().

Referenced by RQ4Metric::compute().

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◆ computeFromArrays() [1/2]

double Chi2Metric::computeFromArrays	(	std::vector< double >	sim_data,
		std::vector< double >	exp_data,
		std::vector< double >	uncertainties,
		std::vector< double >	weight_factors
	)		const

overridevirtual

Computes metric value from data arrays.

Negative values in exp_data are ignored as well as non-positive weight_factors and uncertainties. All arrays involved in the computation must be of the same size.

Parameters

sim_data	array with simulated intensities.
exp_data	array with intensity values obtained from an experiment.
uncertainties	array with experimental data uncertainties.
weight_factors	user-defined weighting factors. Used linearly, no matter which norm is chosen.

Implements ObjectiveMetric.

Definition at line 88 of file ObjectiveMetric.cpp.

 {
     checkIntegrity(sim_data, exp_data, uncertainties, weight_factors);
  
     double result = 0.0;
     auto norm_fun = norm();
     for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i)
         if (exp_data[i] >= 0.0 && weight_factors[i] > 0.0 && uncertainties[i] > 0.0)
             result += norm_fun((exp_data[i] - sim_data[i]) / uncertainties[i]) * weight_factors[i];
  
     return std::isfinite(result) ? result : double_max;
 }

Referenced by RQ4Metric::compute().

◆ computeFromArrays() [2/2]

double Chi2Metric::computeFromArrays	(	std::vector< double >	sim_data,
		std::vector< double >	exp_data,
		std::vector< double >	weight_factors
	)		const

overridevirtual

Computes metric value from data arrays.

Negative values in exp_data are ignored as well as non-positive weight_factors. All arrays involved in the computation must be of the same size.

Parameters

sim_data	array with simulated intensities.
exp_data	array with intensity values obtained from an experiment.
weight_factors	user-defined weighting factors. Used linearly, no matter which norm is chosen.

Implements ObjectiveMetric.

Reimplemented in RelativeDifferenceMetric, and PoissonLikeMetric.

Definition at line 103 of file ObjectiveMetric.cpp.

 {
     checkIntegrity(sim_data, exp_data, weight_factors);
  
     auto norm_fun = norm();
     double result = 0.0;
     for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i)
         if (exp_data[i] >= 0.0 && weight_factors[i] > 0.0)
             result += norm_fun(exp_data[i] - sim_data[i]) * weight_factors[i];
  
     return std::isfinite(result) ? result : double_max;
 }

References ObjectiveMetric::norm().

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◆ norm()

auto ObjectiveMetric::norm ( ) const

inlineinherited

Returns a copy of the normalization function used.

Definition at line 67 of file ObjectiveMetric.h.

67 { return m_norm; }

ObjectiveMetric::m_norm

std::function< double(double)> m_norm

Definition: ObjectiveMetric.h:70

References ObjectiveMetric::m_norm.

Referenced by PoissonLikeMetric::computeFromArrays(), LogMetric::computeFromArrays(), computeFromArrays(), RelativeDifferenceMetric::computeFromArrays(), and ObjectiveMetric::setNorm().

◆ setNorm()

void ObjectiveMetric::setNorm ( std::function< double(double)> norm )

inherited

Definition at line 74 of file ObjectiveMetric.cpp.

 {
     m_norm = std::move(norm);
 }

References ObjectiveMetric::m_norm, and ObjectiveMetric::norm().

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◆ transferToCPP()

virtual void ICloneable::transferToCPP ( )

inlinevirtualinherited

Used for Python overriding of clone (see swig/tweaks.py)

Definition at line 34 of file ICloneable.h.

Member Data Documentation

◆ m_norm

std::function<double(double)> ObjectiveMetric::m_norm

privateinherited

Definition at line 70 of file ObjectiveMetric.h.

Referenced by ObjectiveMetric::norm(), and ObjectiveMetric::setNorm().

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

/home/www/ba/Core/Fitting/ObjectiveMetric.h
/home/www/ba/Core/Fitting/ObjectiveMetric.cpp

Public Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ Chi2Metric()

Member Function Documentation

◆ clone()

◆ compute()

◆ computeFromArrays() [1/2]

◆ computeFromArrays() [2/2]

◆ norm()

◆ setNorm()

◆ transferToCPP()

Member Data Documentation

◆ m_norm