PoissonLikeMetric Class Reference

Inheritance diagram for PoissonLikeMetric:

Collaboration diagram for PoissonLikeMetric:

Public Member Functions
	PoissonLikeMetric ()
PoissonLikeMetric *	clone () const override
double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > weight_factors) 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
double	computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > uncertainties, 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 metric with standard deviation , where is the simulated intensity.

With default L2 norm corresponds to the formula

for unweighted experimental data. Falls to standard Chi2Metric when data uncertainties are taken into account.

Definition at line 109 of file ObjectiveMetric.h.

Constructor & Destructor Documentation

PoissonLikeMetric()

PoissonLikeMetric::PoissonLikeMetric

(

)

Definition at line 120 of file ObjectiveMetric.cpp.

: Chi2Metric() {}

Member Function Documentation

clone()

PoissonLikeMetric * PoissonLikeMetric::clone ( ) const

overridevirtual

Reimplemented from Chi2Metric.

Definition at line 122 of file ObjectiveMetric.cpp.

{
    return copyMetric(*this);
}

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

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computeFromArrays() [1/4]

double PoissonLikeMetric::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.

Reimplemented from Chi2Metric.

Definition at line 127 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 variance = std::max(1.0, sim_data[i]);
        const double value = (sim_data[i] - exp_data[i]) / std::sqrt(variance);
        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, and ObjectiveMetric::norm().

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computeFromArrays() [2/4]

double Chi2Metric::computeFromArrays

override

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.

Definition at line 87 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;
}

computeFromArrays() [3/4]

double Chi2Metric::computeFromArrays

override

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.

Definition at line 98 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;
}

computeFromArrays() [4/4]

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

overridevirtualinherited

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 89 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;
}

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

Referenced by RQ4Metric::compute().

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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.

{ return m_norm; }

References ObjectiveMetric::m_norm.

Referenced by Chi2Metric::computeFromArrays(), LogMetric::computeFromArrays(), 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().

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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