ObjectiveMetric Class Reference

Inheritance diagram for ObjectiveMetric:

Collaboration diagram for ObjectiveMetric:

Public Member Functions
	ObjectiveMetric (std::function< double(double)> norm)
ObjectiveMetric *	clone () const override=0
virtual double	compute (const SimDataPair &data_pair, bool use_weights) const
virtual double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > uncertainties, std::vector< double > weight_factors) const =0
virtual double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > weight_factors) const =0
void	setNorm (std::function< double(double)> norm)
auto	norm () const
virtual void	transferToCPP ()

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

Detailed Description

Base class for metric implementations.

Definition at line 26 of file ObjectiveMetric.h.

Constructor & Destructor Documentation

ObjectiveMetric()

ObjectiveMetric::ObjectiveMetric

(

std::function< double(double)>

norm

)

Definition at line 59 of file ObjectiveMetric.cpp.

: m_norm(std::move(norm)) {}

Member Function Documentation

clone()

ObjectiveMetric* ObjectiveMetric::clone ( ) const

overridepure virtual

Implements ICloneable.

Implemented in RQ4Metric, RelativeDifferenceMetric, LogMetric, PoissonLikeMetric, and Chi2Metric.

compute()

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

virtual

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 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]

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

pure virtual

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.

Implemented in LogMetric, and Chi2Metric.

Referenced by compute().

computeFromArrays() [2/2]

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

pure virtual

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.

Implemented in RelativeDifferenceMetric, LogMetric, PoissonLikeMetric, and Chi2Metric.

setNorm()

void ObjectiveMetric::setNorm

(

std::function< double(double)>

norm

)

Definition at line 75 of file ObjectiveMetric.cpp.

{
    m_norm = std::move(norm);
}

References m_norm, and norm().

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norm()

auto ObjectiveMetric::norm ( ) const

inline

Returns a copy of the normalization function used.

Definition at line 63 of file ObjectiveMetric.h.

{ return m_norm; }

References m_norm.

Referenced by Chi2Metric::computeFromArrays(), LogMetric::computeFromArrays(), PoissonLikeMetric::computeFromArrays(), RelativeDifferenceMetric::computeFromArrays(), and 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

private

Definition at line 66 of file ObjectiveMetric.h.

Referenced by norm(), and setNorm().

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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