/usr/include/OTB-5.8/otbSharkRandomForestsMachineLearningModel.h is in libotb-dev 5.8.0+dfsg-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | /*=========================================================================
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 otbSharkRandomForestsMachineLearningModel_h
#define otbSharkRandomForestsMachineLearningModel_h
#include "otb_shark.h"
#include "itkLightObject.h"
#include "otbMachineLearningModel.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wignored-qualifiers"
#endif
#include "shark/Algorithms/Trainers/RFTrainer.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
/** \class SharkRandomForestsMachineLearningModel
* \brief Shark version of Random Forests algorithm
*
* This is a specialization of MachineLearningModel class allowing to
* use Shark implementation of the Random Forests algorithm.
*
* It is noteworthy that training step is parallel.
*
* For more information, see
* http://image.diku.dk/shark/doxygen_pages/html/classshark_1_1_r_f_trainer.html
*
* \ingroup OTBSupervised
*/
namespace otb
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT SharkRandomForestsMachineLearningModel
: public MachineLearningModel <TInputValue, TTargetValue>
{
public:
/** Standard class typedefs. */
typedef SharkRandomForestsMachineLearningModel 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;
typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
itkTypeMacro(SharkRandomForestsMachineLearningModel, MachineLearningModel);
/** Train the machine learning model */
virtual void Train() ITK_OVERRIDE;
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="") ITK_OVERRIDE;
/** Load the model from file */
virtual 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 ? */
virtual bool CanReadFile(const std::string &) ITK_OVERRIDE;
/** Is the input model file writable and compatible with the corresponding classifier ? */
virtual bool CanWriteFile(const std::string &) ITK_OVERRIDE;
//@}
/** From Shark doc: Get the number of trees to grow.*/
itkGetMacro(NumberOfTrees,unsigned int);
/** From Shark doc: Set the number of trees to grow.*/
itkSetMacro(NumberOfTrees,unsigned int);
/** From Shark doc: Get the number of random attributes to investigate at each node.*/
itkGetMacro(MTry, unsigned int);
/** From Shark doc: Set the number of random attributes to investigate at each node.*/
itkSetMacro(MTry, unsigned int);
/** From Shark doc: Controls when a node is considered pure. If set
* to 1, a node is pure when it only consists of a single node.
*/
itkGetMacro(NodeSize, unsigned int);
/** From Shark doc: Controls when a node is considered pure. If
* set to 1, a node is pure when it only consists of a single node.
*/
itkSetMacro(NodeSize, unsigned int);
/** From Shark doc: Get the fraction of the original training
* dataset to use as the out of bag sample. The default value is
* 0.66.*/
itkGetMacro(OobRatio, float);
/** From Shark doc: Set the fraction of the original training
* dataset to use as the out of bag sample. The default value is 0.66.
*/
itkSetMacro(OobRatio, float);
/** If true, margin confidence value will be computed */
itkGetMacro(ComputeMargin, bool);
/** If true, margin confidence value will be computed */
itkSetMacro(ComputeMargin, bool);
protected:
/** Constructor */
SharkRandomForestsMachineLearningModel();
/** Destructor */
virtual ~SharkRandomForestsMachineLearningModel();
/** Predict values using the model */
virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType *quality=ITK_NULLPTR) const ITK_OVERRIDE;
virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *, ConfidenceListSampleType * = ITK_NULLPTR) const ITK_OVERRIDE;
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
private:
SharkRandomForestsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
shark::RFClassifier m_RFModel;
shark::RFTrainer m_RFTrainer;
unsigned int m_NumberOfTrees;
unsigned int m_MTry;
unsigned int m_NodeSize;
float m_OobRatio;
bool m_ComputeMargin;
/** Confidence list sample */
ConfidenceValueType ComputeConfidence(shark::RealVector & probas,
bool computeMargin) const;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbSharkRandomForestsMachineLearningModel.txx"
#endif
#endif
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