libotb-dev 6.4.0+dfsg-1 / usr / include / OTB-6.4 / otbSVMMachineLearningModel.h

/*
 * Copyright (C) 2005-2017 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 otbSVMMachineLearningModel_h
#define otbSVMMachineLearningModel_h

#include "otbRequiresOpenCVCheck.h"
#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"

#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
#else
class CvSVM;
#endif

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
#ifdef OTB_OPENCV_3
  cv::Ptr<cv::ml::SVM> m_SVMModel;
#else
  CvSVM * m_SVMModel;
#endif
  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