/usr/include/OTB-5.8/otbLibSVMMachineLearningModel.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | /*=========================================================================
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 otbLibSVMMachineLearningModel_txx
#define otbLibSVMMachineLearningModel_txx
#include <fstream>
#include "otbLibSVMMachineLearningModel.h"
namespace otb
{
template <class TInputValue, class TOutputValue>
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::LibSVMMachineLearningModel()
{
m_SVMestimator = SVMEstimatorType::New();
m_SVMestimator->SetSVMType(C_SVC);
m_SVMestimator->SetC(1.0);
m_SVMestimator->SetKernelType(LINEAR);
m_SVMestimator->SetParametersOptimization(false);
m_SVMestimator->DoProbabilityEstimates(false);
//m_SVMestimator->SetEpsilon(1e-6);
this->m_IsRegressionSupported = true;
}
template <class TInputValue, class TOutputValue>
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::~LibSVMMachineLearningModel()
{
//delete m_SVMModel;
}
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::Train()
{
// Set up SVM's parameters
// CvSVMParams params;
// params.svm_type = m_SVMType;
// params.kernel_type = m_KernelType;
// params.term_crit = cvTermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon);
// // Train the SVM
m_SVMestimator->SetInputSampleList(this->GetInputListSample());
m_SVMestimator->SetTrainingSampleList(this->GetTargetListSample());
m_SVMestimator->Update();
this->m_ConfidenceIndex = this->GetDoProbabilityEstimates();
}
template <class TInputValue, class TOutputValue>
typename LibSVMMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::DoPredict(const InputSampleType & input, ConfidenceValueType *quality) const
{
TargetSampleType target;
MeasurementVectorFunctorType mfunctor;
target = m_SVMestimator->GetModel()->EvaluateLabel(mfunctor(input));
if (quality != ITK_NULLPTR)
{
if (!this->m_ConfidenceIndex)
{
itkExceptionMacro("Confidence index not available for this classifier !");
}
typename SVMEstimatorType::ModelType::ProbabilitiesVectorType probaVector =
m_SVMestimator->GetModel()->EvaluateProbabilities(mfunctor(input));
double maxProb = 0.0;
double secProb = 0.0;
for (unsigned int i=0 ; i<probaVector.Size() ; ++i)
{
if (maxProb < probaVector[i])
{
secProb = maxProb;
maxProb = probaVector[i];
}
else if (secProb < probaVector[i])
{
secProb = probaVector[i];
}
}
(*quality) = static_cast<ConfidenceValueType>(maxProb - secProb);
}
return target;
}
template <class TInputValue, class TOutputValue>
void
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::Save(const std::string & filename, const std::string & itkNotUsed(name))
{
m_SVMestimator->GetModel()->SaveModel(filename.c_str());
}
template <class TInputValue, class TOutputValue>
void
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & itkNotUsed(name))
{
m_SVMestimator->GetModel()->LoadModel(filename.c_str());
this->m_ConfidenceIndex = m_SVMestimator->GetModel()->HasProbabilities();
}
template <class TInputValue, class TOutputValue>
bool
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
//TODO: Rework.
std::ifstream ifs;
ifs.open(file.c_str());
if(!ifs)
{
std::cerr<<"Could not read file "<<file<<std::endl;
return false;
}
//Read only the first line.
std::string line;
std::getline(ifs, line);
//if (line.find(m_SVMModel->getName()) != std::string::npos)
if (line.find("svm_type") != std::string::npos)
{
//std::cout<<"Reading a libSVM model"<<std::endl;
return true;
}
ifs.close();
return false;
}
template <class TInputValue, class TOutputValue>
bool
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::CanWriteFile(const std::string & itkNotUsed(file))
{
return false;
}
template <class TInputValue, class TOutputValue>
void
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
// Call superclass implementation
Superclass::PrintSelf(os,indent);
}
} //end namespace otb
#endif
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