/usr/include/OTB-6.4/otbTrainSharkKMeans.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 otbTrainSharkKMeans_txx
#define otbTrainSharkKMeans_txx
#include "otbLearningApplicationBase.h"
#include "otbSharkKMeansMachineLearningModel.h"
namespace otb
{
namespace Wrapper
{
template<class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
{
AddChoice( "classifier.sharkkm", "Shark kmeans classifier" );
SetParameterDescription( "classifier.sharkkm",
"This group of parameters allows setting Shark kMeans classifier parameters. "
"See complete documentation here "
"\\url{http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html}.\n " );
//MaxNumberOfIterations
AddParameter( ParameterType_Int, "classifier.sharkkm.maxiter",
"Maximum number of iteration for the kmeans algorithm." );
SetParameterInt( "classifier.sharkkm.maxiter", 10 );
SetMinimumParameterIntValue( "classifier.sharkkm.maxiter", 0 );
SetParameterDescription( "classifier.sharkkm.maxiter",
"The maximum number of iteration for the kmeans algorithm. 0=unlimited" );
//MaxNumberOfIterations
AddParameter( ParameterType_Int, "classifier.sharkkm.k", "The number of class used for the kmeans algorithm." );
SetParameterInt( "classifier.sharkkm.k", 2 );
SetParameterDescription( "classifier.sharkkm.k",
"The number of class used for the kmeans algorithm. Default set to 2 class" );
SetMinimumParameterIntValue( "classifier.sharkkm.k", 2 );
}
template<class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
{
unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
classifier->SetRegressionMode( this->m_RegressionFlag );
classifier->SetInputListSample( trainingListSample );
classifier->SetTargetListSample( trainingLabeledListSample );
classifier->SetK( k );
classifier->SetMaximumNumberOfIterations( nbMaxIter );
classifier->Train();
classifier->Save( modelPath );
}
} //end namespace wrapper
} //end namespace otb
#endif
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