#include <shark/Data/Dataset.h>
#include <shark/Data/CVDatasetTools.h>
#include <shark/Data/DataDistribution.h>
#include <shark/Data/Statistics.h>
#include <shark/Models/Kernels/ArdKernel.h>
#include <shark/Algorithms/QP/QuadraticProgram.h>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
#include <shark/Algorithms/GradientDescent/Rprop.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/ObjectiveFunctions/SvmLogisticInterpretation.h>
#include <shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h>

Functions
RealVector	run_one_trial (bool verbose)

int	main ()

Variables
unsigned int	useful_dim = 5

unsigned int	noise_dim = 5

unsigned int	total_dim = useful_dim + noise_dim

Function Documentation

◆ main()

int main ( )

Definition at line 180 of file CSvmMaxLikelihoodMS.cpp.

References shark::Data< Type >::element(), shark::meanvar(), run_one_trial(), and total_dim.

◆ run_one_trial()

RealVector run_one_trial ( bool verbose )

Definition at line 23 of file CSvmMaxLikelihoodMS.cpp.

References shark::AbstractSvmTrainer< InputType, unsigned int, KernelClassifier< InputType >, AbstractWeightedTrainer< KernelClassifier< InputType > > >::C(), shark::createCVIID(), shark::ZeroOneLoss< LabelType, OutputType >::eval(), shark::SvmLogisticInterpretation< InputType >::eval(), shark::ARDKernelUnconstrained< InputType >::gammaVector(), shark::LabeledDataDistribution< InputType, LabelType >::generateDataset(), shark::AbstractObjectiveFunction< PointType, ResultT >::init(), shark::IRpropPlus::init(), shark::LabeledData< InputT, LabelT >::inputs(), shark::LabeledData< InputT, LabelT >::labels(), shark::RpropMinus::maxDelta(), noise_dim, shark::ARDKernelUnconstrained< InputType >::parameterVector(), shark::AbstractSvmTrainer< InputType, unsigned int, KernelClassifier< InputType >, AbstractWeightedTrainer< KernelClassifier< InputType > > >::parameterVector(), shark::ResultSet< SearchPointT, ResultT >::point, shark::AbstractSvmTrainer< InputType, unsigned int, KernelClassifier< InputType >, AbstractWeightedTrainer< KernelClassifier< InputType > > >::setParameterVector(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::IRpropPlus::step(), total_dim, shark::NormalizeComponentsUnitVariance< DataType >::train(), shark::CSvmTrainer< InputType, CacheType >::train(), shark::transformInputs(), useful_dim, and shark::ResultSet< SearchPointT, ResultT >::value.

Referenced by main().

Variable Documentation

◆ noise_dim

unsigned int noise_dim = 5

Definition at line 20 of file CSvmMaxLikelihoodMS.cpp.

Referenced by run_one_trial().

◆ total_dim

unsigned int total_dim = useful_dim + noise_dim

Definition at line 21 of file CSvmMaxLikelihoodMS.cpp.

Referenced by main(), and run_one_trial().

◆ useful_dim

unsigned int useful_dim = 5

Definition at line 19 of file CSvmMaxLikelihoodMS.cpp.