/usr/include/OTB-6.4/otbTrainSharkRandomForests.txx is in libotb-dev 6.4.0+dfsg-1.
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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 otbTrainSharkRandomForests_txx
#define otbTrainSharkRandomForests_txx
#include "otbLearningApplicationBase.h"
#include "otbSharkRandomForestsMachineLearningModel.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitSharkRandomForestsParams()
{
AddChoice("classifier.sharkrf", "Shark Random forests classifier");
SetParameterDescription("classifier.sharkrf",
"This group of parameters allows setting Shark Random Forests classifier parameters. "
"See complete documentation here \\url{http://image.diku.dk/shark/doxygen_pages/html/classshark_1_1_r_f_trainer.html}.\n It is noteworthy that training is parallel.");
//MaxNumberOfTrees
AddParameter(ParameterType_Int, "classifier.sharkrf.nbtrees",
"Maximum number of trees in the forest");
SetParameterInt("classifier.sharkrf.nbtrees",100, false);
SetParameterDescription(
"classifier.sharkrf.nbtrees",
"The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. "
"However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. "
"Also to keep in mind, increasing the number of trees increases the prediction time linearly.");
//NodeSize
AddParameter(ParameterType_Int, "classifier.sharkrf.nodesize", "Min size of the node for a split");
SetParameterInt("classifier.sharkrf.nodesize",25, false);
SetParameterDescription(
"classifier.sharkrf.nodesize",
"If the number of samples in a node is smaller than this parameter, "
"then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.");
//MTry
AddParameter(ParameterType_Int, "classifier.sharkrf.mtry", "Number of features tested at each node");
SetParameterInt("classifier.sharkrf.mtry",0, false);
SetParameterDescription(
"classifier.sharkrf.mtry",
"The number of features (variables) which will be tested at each node in "
"order to compute the split. If set to zero, the square root of the number of "
"features is used.");
//OOB Ratio
AddParameter(ParameterType_Float, "classifier.sharkrf.oobr", "Out of bound ratio");
SetParameterFloat("classifier.sharkrf.oobr",0.66, false);
SetParameterDescription("classifier.sharkrf.oobr",
"Set the fraction of the original training dataset to use as the out of bag sample."
"A good default value is 0.66. ");
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainSharkRandomForests(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typedef otb::SharkRandomForestsMachineLearningModel<InputValueType, OutputValueType> SharkRandomForestType;
typename SharkRandomForestType::Pointer classifier = SharkRandomForestType::New();
classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetNodeSize(GetParameterInt("classifier.sharkrf.nodesize"));
classifier->SetOobRatio(GetParameterFloat("classifier.sharkrf.oobr"));
classifier->SetNumberOfTrees(GetParameterInt("classifier.sharkrf.nbtrees"));
classifier->SetMTry(GetParameterInt("classifier.sharkrf.mtry"));
classifier->Train();
classifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
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