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OTB
9.0.0
Orfeo Toolbox
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Go to the documentation of this file.
25 #include "itkEuclideanDistanceMetric.h"
41 template <
class TInputValue,
unsigned int MapDimension>
73 itkTypeMacro(
SOMModel, DimensionalityReductionModel);
76 itkSetMacro(NumberOfIterations,
unsigned int);
77 itkGetMacro(NumberOfIterations,
unsigned int);
78 itkSetMacro(BetaInit,
double);
79 itkGetMacro(BetaInit,
double);
80 itkSetMacro(WriteMap,
bool);
81 itkGetMacro(WriteMap,
bool);
82 itkSetMacro(BetaEnd,
double);
83 itkGetMacro(BetaEnd,
double);
90 itkSetMacro(NeighborhoodSizeInit,
SizeType);
91 itkGetMacro(NeighborhoodSizeInit,
SizeType);
92 itkSetMacro(RandomInit,
bool);
93 itkGetMacro(RandomInit,
bool);
94 itkSetMacro(Seed,
unsigned int);
95 itkGetMacro(Seed,
unsigned int);
98 bool CanReadFile(
const std::string& filename)
override;
99 bool CanWriteFile(
const std::string& filename)
override;
101 void Save(
const std::string& filename,
const std::string& name =
"")
override;
102 void Load(
const std::string& filename,
const std::string& name =
"")
override;
104 void Train()
override;
154 #ifndef OTB_MANUAL_INSTANTIATION
itk::Statistics::ListSample< TargetSampleType > TargetListSampleType
Superclass::ConfidenceValueType ConfidenceValueType
itk::Statistics::ListSample< ProbaSampleType > ProbaListSampleType
Superclass::SizeType SizeType
itk::Statistics::ListSample< InputSampleType > InputListSampleType
MLMSampleTraits< TInputValue >::ValueType InputValueType
Superclass::ProbaListSampleType ProbaListSampleType
Beta behavior over SOM training phase.
The "otb" namespace contains all Orfeo Toolbox (OTB) classes.
InputListSampleType::Pointer ListSamplePointerType
MapType::SizeType SizeType
itk::Statistics::ListSample< ConfidenceSampleType > ConfidenceListSampleType
MapType::SpacingType SpacingType
Superclass::TargetListSampleType TargetListSampleType
SOMLearningBehaviorFunctorType m_BetaFunctor
Superclass::InputSampleType InputSampleType
MachineLearningModel< itk::VariableLengthVector< TInputValue >, itk::VariableLengthVector< TInputValue > > Superclass
SOMMap< itk::VariableLengthVector< TInputValue >, itk::Statistics::EuclideanDistanceMetric< itk::VariableLengthVector< TInputValue > >, MapDimension > MapType
itk::SmartPointer< const Self > ConstPointer
itk::SmartPointer< Self > Pointer
InputValueType m_MaxWeight
unsigned int m_NumberOfIterations
Superclass::InputValueType InputValueType
MLMTargetTraits< double >::SampleType ConfidenceSampleType
Functor::CzihoSOMLearningBehaviorFunctor SOMLearningBehaviorFunctorType
Superclass::SpacingType SpacingType
Superclass::ProbaSampleType ProbaSampleType
Superclass::TargetSampleType TargetSampleType
Superclass::ConfidenceListSampleType ConfidenceListSampleType
MachineLearningModel is the base class for all classifier objects (SVM, KNN, Random Forests,...
itk::SmartPointer< Self > Pointer
MLMTargetTraits< itk::VariableLengthVector< TInputValue > >::ValueType TargetValueType
Functor::CzihoSOMNeighborhoodBehaviorFunctor SOMNeighborhoodBehaviorFunctorType
Superclass::ConfidenceSampleType ConfidenceSampleType
Superclass::TargetValueType TargetValueType
SOMNeighborhoodBehaviorFunctorType m_NeighborhoodSizeFunctor
This class represent a Self Organizing Map.
Superclass::InputListSampleType InputListSampleType
Neighborhood size behavior over SOM training phase.
MLMTargetTraits< TTargetValue >::SampleType TargetSampleType
InputValueType m_MinWeight
MapType::Pointer m_SOMMap