/usr/include/OTB-5.8/otbTrainKNN.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | /*=========================================================================
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 otbTrainKNN_txx
#define otbTrainKNN_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitKNNParams()
{
AddChoice("classifier.knn", "KNN classifier");
SetParameterDescription("classifier.knn", "This group of parameters allows setting KNN classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html}.");
//K parameter
AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
SetParameterInt("classifier.knn.k", 32);
SetParameterDescription("classifier.knn.k","The number of neighbors to use.");
if (this->m_RegressionFlag)
{
// Decision rule : mean / median
AddParameter(ParameterType_Choice, "classifier.knn.rule", "Decision rule");
SetParameterDescription("classifier.knn.rule", "Decision rule for regression output");
AddChoice("classifier.knn.rule.mean", "Mean of neighbors values");
SetParameterDescription("classifier.knn.rule.mean","Returns the mean of neighbors values");
AddChoice("classifier.knn.rule.median", "Median of neighbors values");
SetParameterDescription("classifier.knn.rule.median","Returns the median of neighbors values");
}
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainKNN(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename KNNType::Pointer knnClassifier = KNNType::New();
knnClassifier->SetRegressionMode(this->m_RegressionFlag);
knnClassifier->SetInputListSample(trainingListSample);
knnClassifier->SetTargetListSample(trainingLabeledListSample);
knnClassifier->SetK(GetParameterInt("classifier.knn.k"));
if (this->m_RegressionFlag)
{
std::string decision = this->GetParameterString("classifier.knn.rule");
if (decision == "mean")
{
knnClassifier->SetDecisionRule(KNNType::KNN_MEAN);
}
else if (decision == "median")
{
knnClassifier->SetDecisionRule(KNNType::KNN_MEDIAN);
}
}
knnClassifier->Train();
knnClassifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
|