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/*
* Copyright (C) 2005-2020 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 otbTrainKNN_hxx
#define otbTrainKNN_hxx
#include "otbLearningApplicationBase.h"
#include "otbKNearestNeighborsMachineLearningModel.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitKNNParams()
{
AddChoice("classifier.knn", "KNN classifier");
SetParameterDescription("classifier.knn", "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)
{
typedef otb::KNearestNeighborsMachineLearningModel<InputValueType, OutputValueType> KNNType;
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
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