/usr/include/OTB-6.4/otbSVMMarginSampler.txx is in libotb-dev 6.4.0+dfsg-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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* 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 otbSVMMarginSampler_txx
#define otbSVMMarginSampler_txx
#include "otbSVMMarginSampler.h"
#include "otbMacro.h"
namespace otb
{
template< class TSample, class TModel >
SVMMarginSampler< TSample, TModel >
::SVMMarginSampler()
{
m_NumberOfCandidates = 10;
}
template< class TSample, class TModel >
void
SVMMarginSampler< TSample, TModel >
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
Superclass::PrintSelf(os, indent);
}
template< class TSample, class TModel >
void
SVMMarginSampler< TSample, TModel >
::GenerateData()
{
if(!m_Model)
{
itkExceptionMacro("No model, can not do classification.");
}
if(m_Model->GetNumberOfSupportVectors() == 0)
{
itkExceptionMacro(<<"SVM model does not contain any support vector, can not perform margin sampling.");
}
OutputType* output = const_cast<OutputType*>(this->GetOutput());
output->SetSample(this->GetInput());
this->DoMarginSampling();
}
template< class TSample, class TModel >
void
SVMMarginSampler< TSample, TModel >
::DoMarginSampling()
{
IndexAndDistanceVectorType idDistVector;
OutputType* output = const_cast<OutputType*>(this->GetOutput());
typename TSample::ConstIterator iter = this->GetInput()->Begin();
typename TSample::ConstIterator end = this->GetInput()->End();
typename OutputType::ConstIterator iterO = output->Begin();
typename OutputType::ConstIterator endO = output->End();
typename TSample::MeasurementVectorType measurements;
m_Model->SetConfidenceMode(TModel::CM_HYPER);
int numberOfComponentsPerSample = iter.GetMeasurementVector().Size();
int nbClass = static_cast<int>(m_Model->GetNumberOfClasses());
std::vector<double> hdistances(nbClass * (nbClass - 1) / 2);
otbMsgDevMacro( << "Starting iterations " );
while (iter != end && iterO != endO)
{
int i = 0;
typename SVMModelType::InputSampleType modelMeasurement(numberOfComponentsPerSample);
measurements = iter.GetMeasurementVector();
// otbMsgDevMacro( << "Loop on components " << svm_type );
for (i=0; i<numberOfComponentsPerSample; ++i)
{
modelMeasurement[i] = measurements[i];
}
// Get distances to the hyperplanes
m_Model->Predict(modelMeasurement, &(hdistances[0]));
double minDistance = vcl_abs(hdistances[0]);
// Compute th min distances
for(unsigned int j = 1; j<hdistances.size(); ++j)
{
if(vcl_abs(hdistances[j])<minDistance)
{
minDistance = vcl_abs(hdistances[j]);
}
}
// Keep index and min distance
IndexAndDistanceType value(iter.GetInstanceIdentifier(), minDistance);
idDistVector.push_back(value);
++iter;
++iterO;
}
// Sort index by increasing distances
sort(idDistVector.begin(), idDistVector.end(), &Compare);
// Display the first 10 values
otbMsgDevMacro( <<" Margin Sampling: " );
// Clear previous margin samples
m_MarginSamples.clear();
for(unsigned int i = 0; i<m_NumberOfCandidates && i<idDistVector.size(); ++i)
{
otbMsgDevMacro( "Sample "<<idDistVector[i].first<<" (distance= "<<idDistVector[i].second<<")" )
m_MarginSamples.push_back(idDistVector[i].first);
}
}
} // end of namespace otb
#endif
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