/usr/include/OTB-5.8/otbTrainBoost.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 | /*=========================================================================
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 otbTrainBoost_txx
#define otbTrainBoost_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitBoostParams()
{
AddChoice("classifier.boost", "Boost classifier");
SetParameterDescription("classifier.boost", "This group of parameters allows setting Boost classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/boosting.html}.");
//BoostType
AddParameter(ParameterType_Choice, "classifier.boost.t", "Boost Type");
AddChoice("classifier.boost.t.discrete", "Discrete AdaBoost");
AddChoice("classifier.boost.t.real", "Real AdaBoost (technique using confidence-rated predictions "
"and working well with categorical data)");
AddChoice("classifier.boost.t.logit", "LogitBoost (technique producing good regression fits)");
AddChoice("classifier.boost.t.gentle", "Gentle AdaBoost (technique setting less weight on outlier data points "
"and, for that reason, being often good with regression data)");
SetParameterString("classifier.boost.t", "real");
SetParameterDescription("classifier.boost.t", "Type of Boosting algorithm.");
//Do not expose SplitCriteria
//WeakCount
AddParameter(ParameterType_Int, "classifier.boost.w", "Weak count");
SetParameterInt("classifier.boost.w", 100);
SetParameterDescription("classifier.boost.w","The number of weak classifiers.");
//WeightTrimRate
AddParameter(ParameterType_Float, "classifier.boost.r", "Weight Trim Rate");
SetParameterFloat("classifier.boost.r", 0.95);
SetParameterDescription("classifier.boost.r","A threshold between 0 and 1 used to save computational time. "
"Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. "
"Set this parameter to 0 to turn off this functionality.");
//MaxDepth : Not sure that this parameter has to be exposed.
AddParameter(ParameterType_Int, "classifier.boost.m", "Maximum depth of the tree");
SetParameterInt("classifier.boost.m", 1);
SetParameterDescription("classifier.boost.m","Maximum depth of the tree.");
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainBoost(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename BoostType::Pointer boostClassifier = BoostType::New();
boostClassifier->SetRegressionMode(this->m_RegressionFlag);
boostClassifier->SetInputListSample(trainingListSample);
boostClassifier->SetTargetListSample(trainingLabeledListSample);
boostClassifier->SetBoostType(GetParameterInt("classifier.boost.t"));
boostClassifier->SetWeakCount(GetParameterInt("classifier.boost.w"));
boostClassifier->SetWeightTrimRate(GetParameterFloat("classifier.boost.r"));
boostClassifier->SetMaxDepth(GetParameterInt("classifier.boost.m"));
boostClassifier->Train();
boostClassifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
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