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/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef otbSOMModel_h
#define otbSOMModel_h
#include "otbSOMMap.h"
#include "itkEuclideanDistanceMetric.h" // the distance function
#include "otbCzihoSOMLearningBehaviorFunctor.h"
#include "otbCzihoSOMNeighborhoodBehaviorFunctor.h"
#include "otbMachineLearningModelTraits.h"
#include "otbMachineLearningModel.h"
namespace otb
{
/** \class SOMModel
* MachineLearningModel for Self-Organizing Map
*
* \ingroup OTBDimensionalityReductionLearning
*/
template <class TInputValue, unsigned int MapDimension>
class ITK_EXPORT SOMModel : public MachineLearningModel<itk::VariableLengthVector<TInputValue>, itk::VariableLengthVector<TInputValue>>
{
public:
typedef SOMModel Self;
typedef MachineLearningModel<itk::VariableLengthVector<TInputValue>, itk::VariableLengthVector<TInputValue>> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
typedef typename Superclass::InputValueType InputValueType;
typedef typename Superclass::InputSampleType InputSampleType;
typedef typename Superclass::InputListSampleType InputListSampleType;
typedef typename InputListSampleType::Pointer ListSamplePointerType;
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
// Confidence map related typedefs
typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
typedef typename Superclass::ProbaSampleType ProbaSampleType;
typedef typename Superclass::ProbaListSampleType ProbaListSampleType;
typedef SOMMap<itk::VariableLengthVector<TInputValue>, itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, MapDimension>
MapType;
typedef typename MapType::SizeType SizeType;
typedef typename MapType::SpacingType SpacingType;
typedef Functor::CzihoSOMLearningBehaviorFunctor SOMLearningBehaviorFunctorType;
typedef Functor::CzihoSOMNeighborhoodBehaviorFunctor SOMNeighborhoodBehaviorFunctorType;
itkNewMacro(Self);
itkTypeMacro(SOMModel, DimensionalityReductionModel);
/** Accessors */
itkSetMacro(NumberOfIterations, unsigned int);
itkGetMacro(NumberOfIterations, unsigned int);
itkSetMacro(BetaInit, double);
itkGetMacro(BetaInit, double);
itkSetMacro(WriteMap, bool);
itkGetMacro(WriteMap, bool);
itkSetMacro(BetaEnd, double);
itkGetMacro(BetaEnd, double);
itkSetMacro(MinWeight, InputValueType);
itkGetMacro(MinWeight, InputValueType);
itkSetMacro(MaxWeight, InputValueType);
itkGetMacro(MaxWeight, InputValueType);
itkSetMacro(MapSize, SizeType);
itkGetMacro(MapSize, SizeType);
itkSetMacro(NeighborhoodSizeInit, SizeType);
itkGetMacro(NeighborhoodSizeInit, SizeType);
itkSetMacro(RandomInit, bool);
itkGetMacro(RandomInit, bool);
itkSetMacro(Seed, unsigned int);
itkGetMacro(Seed, unsigned int);
bool CanReadFile(const std::string& filename) override;
bool CanWriteFile(const std::string& filename) override;
void Save(const std::string& filename, const std::string& name = "") override;
void Load(const std::string& filename, const std::string& name = "") override;
void Train() override;
protected:
SOMModel();
~SOMModel() override;
private:
typename MapType::Pointer m_SOMMap;
virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
/** Map size (width, height) */
SizeType m_MapSize{0,0};
/** Number of iterations */
unsigned int m_NumberOfIterations;
/** Initial learning coefficient */
double m_BetaInit;
/** Final learning coefficient */
double m_BetaEnd;
/** Initial neighborhood size */
SizeType m_NeighborhoodSizeInit{0,0};
/** Minimum initial neuron weights */
InputValueType m_MinWeight;
/** Maximum initial neuron weights */
InputValueType m_MaxWeight;
/** Random initialization bool */
bool m_RandomInit;
/** Seed for random initialization */
unsigned int m_Seed;
/** Behavior of the Learning weightening (link to the beta coefficient) */
SOMLearningBehaviorFunctorType m_BetaFunctor;
/** Behavior of the Neighborhood extent */
SOMNeighborhoodBehaviorFunctorType m_NeighborhoodSizeFunctor;
/** Write the SOM Map vectors in a txt file */
bool m_WriteMap;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbSOMModel.hxx"
#endif
#endif
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