Inheritance diagram for LogMetric:

Collaboration diagram for LogMetric:

Public Member Functions
	LogMetric ()

LogMetric *	clone () const override

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

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

virtual double	compute (const SimDataPair &data_pair, bool use_weights) const

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

auto	norm () const

virtual void	transferToCPP ()

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

Detailed Description

Implementation of the standard $\chi^2$ metric with intensity and experimental data being replaced by $\log_{10} I$ and $\log_{10} D$ accordingly.

With default L2 norm corresponds to the formula

$\chi^2 = \sum \frac{(\log_{10} I - log_{10} D)^2 D^2 \ln^2{10}}{\delta_D^2}$

Definition at line 133 of file ObjectiveMetric.h.

Constructor & Destructor Documentation

◆ LogMetric()

LogMetric::LogMetric ( )

Definition at line 148 of file ObjectiveMetric.cpp.

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

ObjectiveMetric::ObjectiveMetric

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

Definition: ObjectiveMetric.cpp:59

ObjectiveMetricUtils::l2Norm

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

Returns L2 normalization function.

Definition: ObjectiveMetricUtils.cpp:54

Member Function Documentation

◆ clone()

LogMetric * LogMetric::clone ( ) const

overridevirtual

Implements ObjectiveMetric.

Definition at line 150 of file ObjectiveMetric.cpp.

 {
     return copyMetric(*this);
 }

References anonymous_namespace{ObjectiveMetric.cpp}::copyMetric().

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◆ computeFromArrays() [1/2]

double LogMetric::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 155 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 (weight_factors[i] <= 0.0 || exp_data[i] < 0.0 || uncertainties[i] <= 0.0)
             continue;
         const double sim_val = std::max(double_min, sim_data[i]);
         const double exp_val = std::max(double_min, exp_data[i]);
         double value = std::log10(sim_val) - std::log10(exp_val);
         value *= exp_val * ln10 / uncertainties[i];
         result += norm_fun(value) * weight_factors[i];
     }
  
     return std::isfinite(result) ? result : double_max;
 }

References anonymous_namespace{ObjectiveMetric.cpp}::checkIntegrity(), anonymous_namespace{ObjectiveMetric.cpp}::double_max, anonymous_namespace{ObjectiveMetric.cpp}::double_min, anonymous_namespace{ObjectiveMetric.cpp}::ln10, and ObjectiveMetric::norm().

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◆ computeFromArrays() [2/2]

double LogMetric::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.

Definition at line 176 of file ObjectiveMetric.cpp.

 {
     checkIntegrity(sim_data, exp_data, 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 (weight_factors[i] <= 0.0 || exp_data[i] < 0.0)
             continue;
         const double sim_val = std::max(double_min, sim_data[i]);
         const double exp_val = std::max(double_min, exp_data[i]);
         result += norm_fun(std::log10(sim_val) - std::log10(exp_val)) * weight_factors[i];
     }
  
     return std::isfinite(result) ? result : double_max;
 }

References anonymous_namespace{ObjectiveMetric.cpp}::checkIntegrity(), anonymous_namespace{ObjectiveMetric.cpp}::double_max, anonymous_namespace{ObjectiveMetric.cpp}::double_min, and ObjectiveMetric::norm().

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◆ 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 61 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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◆ setNorm()

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

inherited

Definition at line 75 of file ObjectiveMetric.cpp.

 {
     m_norm = std::move(norm);
 }

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

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◆ norm()

auto ObjectiveMetric::norm ( ) const

inlineinherited

Returns a copy of the normalization function used.

Definition at line 63 of file ObjectiveMetric.h.

63 { return m_norm; }

References ObjectiveMetric::m_norm.

Referenced by Chi2Metric::computeFromArrays(), computeFromArrays(), PoissonLikeMetric::computeFromArrays(), RelativeDifferenceMetric::computeFromArrays(), and ObjectiveMetric::setNorm().

◆ 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 66 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

◆ LogMetric()

Member Function Documentation

◆ clone()

◆ computeFromArrays() [1/2]

◆ computeFromArrays() [2/2]

◆ compute()

◆ setNorm()

◆ norm()

◆ transferToCPP()

Member Data Documentation

◆ m_norm