/usr/include/OTB-5.8/otbSVMMachineLearningModel.h is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef otbSVMMachineLearningModel_h
#define otbSVMMachineLearningModel_h
#include "otbRequiresOpenCVCheck.h"
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
class CvSVM;
namespace otb
{
/**
* \brief OpenCV implementation of SVM algorithm.
*
* This machine learning model uses the OpenCV implementation of the
* SVM algorithm. Since this implementation is buggy in the linear
* case, we recommend users to use the LibSVM implementation instead,
* through the otb::LibSVMMachineLearningModel.
*/
template <class TInputValue, class TTargetValue>
class ITK_EXPORT SVMMachineLearningModel
: public MachineLearningModel <TInputValue, TTargetValue>
{
public:
/** Standard class typedefs. */
typedef SVMMachineLearningModel Self;
typedef MachineLearningModel<TInputValue, TTargetValue> 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 Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
/** Train the machine learning model */
void Train() ITK_OVERRIDE;
/** Save the model to file */
void Save(const std::string & filename, const std::string & name="") ITK_OVERRIDE;
/** Load the model from file */
void Load(const std::string & filename, const std::string & name="") ITK_OVERRIDE;
/**\name Classification model file compatibility tests */
//@{
/** Is the input model file readable and compatible with the corresponding classifier ? */
bool CanReadFile(const std::string &) ITK_OVERRIDE;
/** Is the input model file writable and compatible with the corresponding classifier ? */
bool CanWriteFile(const std::string &) ITK_OVERRIDE;
//@}
//Setters/Getters to SVM model
itkGetMacro(SVMType, int);
itkSetMacro(SVMType, int);
itkGetMacro(KernelType, int);
itkSetMacro(KernelType, int);
//CV_TERMCRIT_ITER or CV_TERMCRIT_EPS
itkGetMacro(TermCriteriaType, int);
itkSetMacro(TermCriteriaType, int);
itkGetMacro(MaxIter, int);
itkSetMacro(MaxIter, int);
itkGetMacro(Epsilon, double);
itkSetMacro(Epsilon, double);
// for poly
itkGetMacro(Degree, double);
itkSetMacro(Degree, double);
itkGetMacro(OutputDegree, double);
// for poly/rbf/sigmoid
itkGetMacro(Gamma, double);
itkSetMacro(Gamma, double);
itkGetMacro(OutputGamma, double);
// for poly/sigmoid
itkGetMacro(Coef0, double);
itkSetMacro(Coef0, double);
itkGetMacro(OutputCoef0, double);
// for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
itkGetMacro(C, double);
itkSetMacro(C, double);
itkGetMacro(OutputC, double);
// for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
itkGetMacro(Nu, double);
itkSetMacro(Nu, double);
itkGetMacro(OutputNu, double);
// for CV_SVM_EPS_SVR
itkGetMacro(P, double);
itkSetMacro(P, double);
itkGetMacro(OutputP, double);
itkGetMacro(ParameterOptimization, bool);
itkSetMacro(ParameterOptimization, bool);
protected:
/** Constructor */
SVMMachineLearningModel();
/** Destructor */
~SVMMachineLearningModel() ITK_OVERRIDE;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType *quality=ITK_NULLPTR) const ITK_OVERRIDE;
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const ITK_OVERRIDE;
private:
SVMMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
CvSVM * m_SVMModel;
int m_SVMType;
int m_KernelType;
double m_Degree;
double m_Gamma;
double m_Coef0;
double m_C;
double m_Nu;
double m_P;
int m_TermCriteriaType;
int m_MaxIter;
double m_Epsilon;
bool m_ParameterOptimization;
//Output parameters
double m_OutputDegree;
double m_OutputGamma;
double m_OutputCoef0;
double m_OutputC;
double m_OutputNu;
double m_OutputP;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbSVMMachineLearningModel.txx"
#endif
#endif
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