BornAgain  1.19.79
Open-source research software to simulate and fit neutron and x-ray reflectometry and grazing-incidence small-angle scattering
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LogMetric Class Reference

Description

Implementation of the standard $ \chi^2 $ metric with intensity $I$ and experimental data $D$ 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 135 of file ObjectiveMetric.h.

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Public Member Functions

 LogMetric ()
 
LogMetric * clone () const override
 
virtual double compute (const SimDataPair &data_pair, bool use_weights) const
 Computes metric value from SimDataPair object. Calls computeFromArrays internally. More...
 
double computeFromArrays (std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > exp_stdv, std::vector< double > weight_factors) const 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. 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. 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. 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
 

Constructor & Destructor Documentation

◆ LogMetric()

LogMetric::LogMetric ( )

Definition at line 158 of file ObjectiveMetric.cpp.

159  : ObjectiveMetric(ObjectiveMetricUtils::l2Norm())
160 {
161 }
ObjectiveMetric::ObjectiveMetric
ObjectiveMetric(std::function< double(double)> norm)
Definition: ObjectiveMetric.cpp:61
ObjectiveMetricUtils::l2Norm
std::function< double(double)> l2Norm()
Returns L2 normalization function.
Definition: ObjectiveMetricUtils.cpp:56

Member Function Documentation

◆ clone()

LogMetric * LogMetric::clone ( ) const
overridevirtual

Implements ObjectiveMetric.

Definition at line 163 of file ObjectiveMetric.cpp.

164 {
165  return copyMetric(*this);
166 }

◆ compute()

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

Computes metric value from SimDataPair object. Calls computeFromArrays internally.

Parameters
data_pairSimDataPair object. Can optionally contain data uncertainties
use_weightsboolean, defines if data uncertainties should be taken into account

Reimplemented in RQ4Metric.

Definition at line 66 of file ObjectiveMetric.cpp.

67 {
68  if (use_weights && !data_pair.containsUncertainties())
69  throw std::runtime_error("Error in ObjectiveMetric::compute: the metric is weighted, but "
70  "the simulation-data pair does not contain uncertainties");
71 
72  if (use_weights)
73  return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
74  data_pair.uncertainties_array(), data_pair.user_weights_array());
75  return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
76  data_pair.user_weights_array());
77 }
ObjectiveMetric::computeFromArrays
virtual double computeFromArrays(std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > exp_stdv, std::vector< double > weight_factors) const =0
Computes metric value from data arrays. Negative values in exp_data are ignored as well as non-positi...
SimDataPair::experimental_array
std::vector< double > experimental_array() const
Returns the flattened experimental data cut to the ROI area.
Definition: SimDataPair.cpp:219
SimDataPair::user_weights_array
std::vector< double > user_weights_array() const
Returns a flat array of user weights cut to the ROI area.
Definition: SimDataPair.cpp:240
SimDataPair::uncertainties_array
std::vector< double > uncertainties_array() const
Returns the flattened experimental uncertainties cut to the ROI area. If no uncertainties are availab...
Definition: SimDataPair.cpp:233
SimDataPair::simulation_array
std::vector< double > simulation_array() const
Returns the flattened simulated intensities cut to the ROI area.
Definition: SimDataPair.cpp:226
SimDataPair::containsUncertainties
bool containsUncertainties() const
Definition: SimDataPair.cpp:159

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 LogMetric::computeFromArrays ( std::vector< double >  sim_data,
std::vector< double >  exp_data,
std::vector< double >  exp_stdv,
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_dataarray with simulated intensities.
exp_dataarray with intensity values obtained from an experiment.
exp_stdvarray with experimental data uncertainties.
weight_factorsuser-defined weighting factors. Used linearly, no matter which norm is chosen.

Implements ObjectiveMetric.

Definition at line 168 of file ObjectiveMetric.cpp.

171 {
172  checkIntegrity(sim_data, exp_data, exp_stdv, weight_factors);
173 
174  double result = 0.0;
175  auto norm_fun = norm();
176  for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i) {
177  if (weight_factors[i] <= 0.0 || exp_data[i] < 0.0 || exp_stdv[i] <= 0.0)
178  continue;
179  const double sim_val = std::max(double_min, sim_data[i]);
180  const double exp_val = std::max(double_min, exp_data[i]);
181  double value = std::log10(sim_val) - std::log10(exp_val);
182  value *= exp_val * ln10 / exp_stdv[i];
183  result += norm_fun(value) * weight_factors[i];
184  }
185 
186  return std::isfinite(result) ? result : double_max;
187 }
ObjectiveMetric::norm
auto norm() const
Returns a copy of the normalization function used.
Definition: ObjectiveMetric.h:67

References 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_dataarray with simulated intensities.
exp_dataarray with intensity values obtained from an experiment.
weight_factorsuser-defined weighting factors. Used linearly, no matter which norm is chosen.

Implements ObjectiveMetric.

Definition at line 189 of file ObjectiveMetric.cpp.

191 {
192  checkIntegrity(sim_data, exp_data, weight_factors);
193 
194  double result = 0.0;
195  auto norm_fun = norm();
196  for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i) {
197  if (weight_factors[i] <= 0.0 || exp_data[i] < 0.0)
198  continue;
199  const double sim_val = std::max(double_min, sim_data[i]);
200  const double exp_val = std::max(double_min, exp_data[i]);
201  result += norm_fun(std::log10(sim_val) - std::log10(exp_val)) * weight_factors[i];
202  }
203 
204  return std::isfinite(result) ? result : double_max;
205 }

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 computeFromArrays(), Chi2Metric::computeFromArrays(), PoissonLikeMetric::computeFromArrays(), meanRelativeDifferenceMetric::computeFromArrays(), and ObjectiveMetric::setNorm().

◆ setNorm()

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

Definition at line 79 of file ObjectiveMetric.cpp.

80 {
81  m_norm = std::move(norm);
82 }

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 32 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: