/usr/include/OTB-5.8/otbTrainDecisionTree.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | /*=========================================================================
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 otbTrainDecisionTree_txx
#define otbTrainDecisionTree_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitDecisionTreeParams()
{
AddChoice("classifier.dt", "Decision Tree classifier");
SetParameterDescription("classifier.dt",
"This group of parameters allows setting Decision Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/decision_trees.html}.");
//MaxDepth
AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
SetParameterInt("classifier.dt.max", 65535);
SetParameterDescription(
"classifier.dt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
"possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "
"if the tree is pruned.");
//MinSampleCount
AddParameter(ParameterType_Int, "classifier.dt.min", "Minimum number of samples in each node");
SetParameterInt("classifier.dt.min", 10);
SetParameterDescription("classifier.dt.min", "If the number of samples in a node is smaller than this parameter, "
"then this node will not be split.");
//RegressionAccuracy
AddParameter(ParameterType_Float, "classifier.dt.ra", "Termination criteria for regression tree");
SetParameterFloat("classifier.dt.ra", 0.01);
SetParameterDescription("classifier.dt.min", "If all absolute differences between an estimated value in a node "
"and the values of the train samples in this node are smaller than this regression accuracy parameter, "
"then the node will not be split.");
//UseSurrogates : don't need to be exposed !
//AddParameter(ParameterType_Empty, "classifier.dt.sur", "Surrogate splits will be built");
//SetParameterDescription("classifier.dt.sur","These splits allow working with missing data and compute variable importance correctly.");
//MaxCategories
AddParameter(ParameterType_Int, "classifier.dt.cat",
"Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split");
SetParameterInt("classifier.dt.cat", 10);
SetParameterDescription(
"classifier.dt.cat",
"Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.");
//CVFolds
AddParameter(ParameterType_Int, "classifier.dt.f", "K-fold cross-validations");
SetParameterInt("classifier.dt.f", 10);
SetParameterDescription(
"classifier.dt.f", "If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds.");
//Use1seRule
AddParameter(ParameterType_Empty, "classifier.dt.r", "Set Use1seRule flag to false");
SetParameterDescription(
"classifier.dt.r",
"If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.");
//TruncatePrunedTree
AddParameter(ParameterType_Empty, "classifier.dt.t", "Set TruncatePrunedTree flag to false");
SetParameterDescription("classifier.dt.t", "If true, then pruned branches are physically removed from the tree.");
//Priors are not exposed.
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainDecisionTree(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename DecisionTreeType::Pointer classifier = DecisionTreeType::New();
classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetMaxDepth(GetParameterInt("classifier.dt.max"));
classifier->SetMinSampleCount(GetParameterInt("classifier.dt.min"));
classifier->SetRegressionAccuracy(GetParameterFloat("classifier.dt.ra"));
classifier->SetMaxCategories(GetParameterInt("classifier.dt.cat"));
classifier->SetCVFolds(GetParameterInt("classifier.dt.f"));
if (IsParameterEnabled("classifier.dt.r"))
{
classifier->SetUse1seRule(false);
}
if (IsParameterEnabled("classifier.dt.t"))
{
classifier->SetTruncatePrunedTree(false);
}
classifier->Train();
classifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
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