/usr/include/OTB-5.8/otbTrainLibSVM.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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | /*=========================================================================
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 otbTrainLibSVM_txx
#define otbTrainLibSVM_txx
#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::InitLibSVMParams()
{
AddChoice("classifier.libsvm", "LibSVM classifier");
SetParameterDescription("classifier.libsvm", "This group of parameters allows setting SVM classifier parameters.");
AddParameter(ParameterType_Choice, "classifier.libsvm.k", "SVM Kernel Type");
AddChoice("classifier.libsvm.k.linear", "Linear");
AddChoice("classifier.libsvm.k.rbf", "Gaussian radial basis function");
AddChoice("classifier.libsvm.k.poly", "Polynomial");
AddChoice("classifier.libsvm.k.sigmoid", "Sigmoid");
SetParameterString("classifier.libsvm.k", "linear");
SetParameterDescription("classifier.libsvm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Choice, "classifier.libsvm.m", "SVM Model Type");
SetParameterDescription("classifier.libsvm.m", "Type of SVM formulation.");
if (this->m_RegressionFlag)
{
AddChoice("classifier.libsvm.m.epssvr", "Epsilon Support Vector Regression");
AddChoice("classifier.libsvm.m.nusvr", "Nu Support Vector Regression");
SetParameterString("classifier.libsvm.m", "epssvr");
}
else
{
AddChoice("classifier.libsvm.m.csvc", "C support vector classification");
AddChoice("classifier.libsvm.m.nusvc", "Nu support vector classification");
AddChoice("classifier.libsvm.m.oneclass", "Distribution estimation (One Class SVM)");
SetParameterString("classifier.libsvm.m", "csvc");
}
AddParameter(ParameterType_Float, "classifier.libsvm.c", "Cost parameter C");
SetParameterFloat("classifier.libsvm.c", 1.0);
SetParameterDescription(
"classifier.libsvm.c",
"SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.");
AddParameter(ParameterType_Empty, "classifier.libsvm.opt", "Parameters optimization");
MandatoryOff("classifier.libsvm.opt");
SetParameterDescription("classifier.libsvm.opt", "SVM parameters optimization flag.");
AddParameter(ParameterType_Empty, "classifier.libsvm.prob", "Probability estimation");
MandatoryOff("classifier.libsvm.prob");
SetParameterDescription("classifier.libsvm.prob", "Probability estimation flag.");
if (this->m_RegressionFlag)
{
AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
SetParameterFloat("classifier.libsvm.eps", 1e-3);
AddParameter(ParameterType_Float, "classifier.libsvm.nu", "Nu");
SetParameterFloat("classifier.libsvm.nu", 0.5);
}
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainLibSVM(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typename LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
libSVMClassifier->SetRegressionMode(this->m_RegressionFlag);
libSVMClassifier->SetInputListSample(trainingListSample);
libSVMClassifier->SetTargetListSample(trainingLabeledListSample);
//SVM Option
//TODO : Add other options ?
if (IsParameterEnabled("classifier.libsvm.opt"))
{
libSVMClassifier->SetParameterOptimization(true);
}
if (IsParameterEnabled("classifier.libsvm.prob"))
{
libSVMClassifier->SetDoProbabilityEstimates(true);
}
libSVMClassifier->SetC(GetParameterFloat("classifier.libsvm.c"));
switch (GetParameterInt("classifier.libsvm.k"))
{
case 0: // LINEAR
libSVMClassifier->SetKernelType(LINEAR);
break;
case 1: // RBF
libSVMClassifier->SetKernelType(RBF);
break;
case 2: // POLY
libSVMClassifier->SetKernelType(POLY);
break;
case 3: // SIGMOID
libSVMClassifier->SetKernelType(SIGMOID);
break;
default: // DEFAULT = LINEAR
libSVMClassifier->SetKernelType(LINEAR);
break;
}
if (this->m_RegressionFlag)
{
switch (GetParameterInt("classifier.libsvm.m"))
{
case 0: // EPSILON_SVR
libSVMClassifier->SetSVMType(EPSILON_SVR);
break;
case 1: // NU_SVR
libSVMClassifier->SetSVMType(NU_SVR);
break;
default:
libSVMClassifier->SetSVMType(EPSILON_SVR);
break;
}
libSVMClassifier->SetEpsilon(GetParameterFloat("classifier.libsvm.eps"));
libSVMClassifier->SetNu(GetParameterFloat("classifier.libsvm.nu"));
}
else
{
switch (GetParameterInt("classifier.libsvm.m"))
{
case 0: // C_SVC
libSVMClassifier->SetSVMType(C_SVC);
break;
case 1: // NU_SVC
libSVMClassifier->SetSVMType(NU_SVC);
break;
case 2: // ONE_CLASS
libSVMClassifier->SetSVMType(ONE_CLASS);
break;
default:
libSVMClassifier->SetSVMType(C_SVC);
break;
}
}
libSVMClassifier->Train();
libSVMClassifier->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif
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