/usr/include/OTB-5.8/otbDecisionTreeMachineLearningModel.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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | /*=========================================================================
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 otbDecisionTreeMachineLearningModel_txx
#define otbDecisionTreeMachineLearningModel_txx
#include "otbDecisionTreeMachineLearningModel.h"
#include "otbOpenCVUtils.h"
#include <fstream>
#include "itkMacro.h"
namespace otb
{
template <class TInputValue, class TOutputValue>
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::DecisionTreeMachineLearningModel() :
m_DTreeModel (new CvDTree),
m_MaxDepth(INT_MAX),
m_MinSampleCount(10),
m_RegressionAccuracy(0.01),
m_UseSurrogates(true),
m_MaxCategories(10),
m_CVFolds(10),
m_Use1seRule(true),
m_TruncatePrunedTree(true)
{
this->m_IsRegressionSupported = true;
}
template <class TInputValue, class TOutputValue>
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::~DecisionTreeMachineLearningModel()
{
delete m_DTreeModel;
}
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(),labels);
float * priors = m_Priors.empty() ? ITK_NULLPTR : &m_Priors.front();
CvDTreeParams params = CvDTreeParams(m_MaxDepth, m_MinSampleCount, m_RegressionAccuracy,
m_UseSurrogates, m_MaxCategories, m_CVFolds, m_Use1seRule, m_TruncatePrunedTree, priors);
//train the Decision Tree model
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
if (!this->m_RegressionMode) //Classification
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
m_DTreeModel->train(samples,CV_ROW_SAMPLE,labels,cv::Mat(),cv::Mat(),var_type,cv::Mat(),params);
}
template <class TInputValue, class TOutputValue>
typename DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::DoPredict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input,sample);
double result = m_DTreeModel->predict(sample, cv::Mat(), false)->value;
TargetSampleType target;
target[0] = static_cast<TOutputValue>(result);
if (quality != ITK_NULLPTR)
{
if (!this->m_ConfidenceIndex)
{
itkExceptionMacro("Confidence index not available for this classifier !");
}
}
return target;
}
template <class TInputValue, class TOutputValue>
void
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::Save(const std::string & filename, const std::string & name)
{
if (name == "")
m_DTreeModel->save(filename.c_str(), ITK_NULLPTR);
else
m_DTreeModel->save(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
void
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & name)
{
if (name == "")
m_DTreeModel->load(filename.c_str(), ITK_NULLPTR);
else
m_DTreeModel->load(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
bool
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
std::ifstream ifs;
ifs.open(file.c_str());
if(!ifs)
{
std::cerr<<"Could not read file "<<file<<std::endl;
return false;
}
while (!ifs.eof())
{
std::string line;
std::getline(ifs, line);
//if (line.find(m_SVMModel->getName()) != std::string::npos)
if (line.find(CV_TYPE_NAME_ML_TREE) != std::string::npos)
{
//std::cout<<"Reading a "<<CV_TYPE_NAME_ML_TREE<<" model"<<std::endl;
return true;
}
}
ifs.close();
return false;
}
template <class TInputValue, class TOutputValue>
bool
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::CanWriteFile(const std::string & itkNotUsed(file))
{
return false;
}
template <class TInputValue, class TOutputValue>
void
DecisionTreeMachineLearningModel<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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