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LogMetric Class Reference

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. More...

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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. 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 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.

Constructor & Destructor Documentation

◆ LogMetric()

LogMetric::LogMetric ( )

Definition at line 147 of file ObjectiveMetric.cpp.

147 : 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()

LogMetric * LogMetric::clone ( ) const
overridevirtual

Implements ObjectiveMetric.

Definition at line 149 of file ObjectiveMetric.cpp.

150 {
151  return copyMetric(*this);
152 }

◆ 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 60 of file ObjectiveMetric.cpp.

61 {
62  if (use_weights && !data_pair.containsUncertainties())
63  throw std::runtime_error("Error in ObjectiveMetric::compute: the metric is weighted, but "
64  "the simulation-data pair does not contain uncertainties");
65 
66  if (use_weights)
67  return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
68  data_pair.uncertainties_array(), data_pair.user_weights_array());
69  else
70  return computeFromArrays(data_pair.simulation_array(), data_pair.experimental_array(),
71  data_pair.user_weights_array());
72 }
ObjectiveMetric::computeFromArrays
virtual double computeFromArrays(std::vector< double > sim_data, std::vector< double > exp_data, std::vector< double > uncertainties, std::vector< double > weight_factors) const =0
Computes metric value from data arrays.
SimDataPair::experimental_array
std::vector< double > experimental_array() const
Returns the flattened experimental data cut to the ROI area.
Definition: SimDataPair.cpp:151
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:172
SimDataPair::uncertainties_array
std::vector< double > uncertainties_array() const
Returns the flattened experimental uncertainties cut to the ROI area.
Definition: SimDataPair.cpp:165
SimDataPair::simulation_array
std::vector< double > simulation_array() const
Returns the flattened simulated intensities cut to the ROI area.
Definition: SimDataPair.cpp:158
SimDataPair::containsUncertainties
bool containsUncertainties() const
Definition: SimDataPair.cpp:86

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

Implements ObjectiveMetric.

Definition at line 154 of file ObjectiveMetric.cpp.

157 {
158  checkIntegrity(sim_data, exp_data, uncertainties, weight_factors);
159 
160  double result = 0.0;
161  auto norm_fun = norm();
162  for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i) {
163  if (weight_factors[i] <= 0.0 || exp_data[i] < 0.0 || uncertainties[i] <= 0.0)
164  continue;
165  const double sim_val = std::max(double_min, sim_data[i]);
166  const double exp_val = std::max(double_min, exp_data[i]);
167  double value = std::log10(sim_val) - std::log10(exp_val);
168  value *= exp_val * ln10 / uncertainties[i];
169  result += norm_fun(value) * weight_factors[i];
170  }
171 
172  return std::isfinite(result) ? result : double_max;
173 }
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 175 of file ObjectiveMetric.cpp.

177 {
178  checkIntegrity(sim_data, exp_data, weight_factors);
179 
180  double result = 0.0;
181  auto norm_fun = norm();
182  for (size_t i = 0, sim_size = sim_data.size(); i < sim_size; ++i) {
183  if (weight_factors[i] <= 0.0 || exp_data[i] < 0.0)
184  continue;
185  const double sim_val = std::max(double_min, sim_data[i]);
186  const double exp_val = std::max(double_min, exp_data[i]);
187  result += norm_fun(std::log10(sim_val) - std::log10(exp_val)) * weight_factors[i];
188  }
189 
190  return std::isfinite(result) ? result : double_max;
191 }

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(), computeFromArrays(), Chi2Metric::computeFromArrays(), RelativeDifferenceMetric::computeFromArrays(), and ObjectiveMetric::setNorm().

◆ setNorm()

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

Definition at line 74 of file ObjectiveMetric.cpp.

75 {
76  m_norm = std::move(norm);
77 }

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: