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Manuel Grizonnet authored
OTB followed since the beginning the ITK convention and use .txx extension for all template classes. Nevertheless, some development tools do not recognize .txx file extension. Other tool like GitHub can't do in-browser syntax highlighting for txx files I think. The root problem is the use of the txx which should be changed to hxx (or hpp). In 2011, after an in-depth discussion near April 20, 2011 on the Insight-Developers mailing list, ITK rename all txx files to hxx (and event prevent the push of .txx files with a pre-commit hook). It happens is major release v4. You can find some arguments in the discussion about the change and also in other projects related to ITK which applied the same modification, see for instance VXL: https://github.com/vxl/vxl/issues/209 This commit apply now the same modification for OTB. I understand that it will change some habit for developers and don't bring new features but I think that in general it is better to stay align with ITK guidelines. In my opinion, it always facilitate the use of OTB and ITK together if we share when we can the same code architecture, directory organization, naming conventions...
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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 otbTrainLibSVM_txx
#define otbTrainLibSVM_txx
#include "otbLearningApplicationBase.h"
#include "otbLibSVMMachineLearningModel.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");
SetParameterDescription("classifier.libsvm.k.linear",
"Linear Kernel, no mapping is done, this is the fastest option.");
AddChoice("classifier.libsvm.k.rbf", "Gaussian radial basis function");
SetParameterDescription("classifier.libsvm.k.rbf",
"This kernel is a good choice in most of the case. It is "
"an exponential function of the euclidian distance between "
"the vectors.");
AddChoice("classifier.libsvm.k.poly", "Polynomial");
SetParameterDescription("classifier.libsvm.k.poly",
"Polynomial Kernel, the mapping is a polynomial function.");
AddChoice("classifier.libsvm.k.sigmoid", "Sigmoid");
SetParameterDescription("classifier.libsvm.k.sigmoid",
"The kernel is a hyperbolic tangente function of the vectors.");
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");
SetParameterDescription("classifier.libsvm.m.epssvr",
"The distance between feature vectors from the training set and the "
"fitting hyper-plane must be less than Epsilon. For outliers the penalty "
"multiplier C is used ");
AddChoice("classifier.libsvm.m.nusvr", "Nu Support Vector Regression");
SetParameterString("classifier.libsvm.m", "epssvr");
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SetParameterDescription("classifier.libsvm.m.nusvr",
"Same as the epsilon regression except that this time the bounded "
"parameter nu is used instead of epsilon");
}
else
{
AddChoice("classifier.libsvm.m.csvc", "C support vector classification");
SetParameterDescription("classifier.libsvm.m.csvc",
"This formulation allows imperfect separation of classes. The penalty "
"is set through the cost parameter C.");
AddChoice("classifier.libsvm.m.nusvc", "Nu support vector classification");
SetParameterDescription("classifier.libsvm.m.nusvc",
"This formulation allows imperfect separation of classes. The penalty "
"is set through the cost parameter Nu. As compared to C, Nu is harder "
"to optimize, and may not be as fast.");
AddChoice("classifier.libsvm.m.oneclass", "Distribution estimation (One Class SVM)");
SetParameterDescription("classifier.libsvm.m.oneclass",
"All the training data are from the same class, SVM builds a boundary "
"that separates the class from the rest of the feature space.");
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_Float, "classifier.libsvm.nu", "Cost parameter Nu");
SetParameterFloat("classifier.libsvm.nu",0.5);
SetParameterDescription("classifier.libsvm.nu",
"Cost parameter Nu, in the range 0..1, the larger the value, "
"the smoother the decision.");
// It seems that it miss a nu parameter for the nu-SVM use.
AddParameter(ParameterType_Bool, "classifier.libsvm.opt", "Parameters optimization");
SetParameterDescription("classifier.libsvm.opt", "SVM parameters optimization flag.");
AddParameter(ParameterType_Bool, "classifier.libsvm.prob", "Probability estimation");
SetParameterDescription("classifier.libsvm.prob", "Probability estimation flag.");
if (this->m_RegressionFlag)
{
AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
SetParameterFloat("classifier.libsvm.eps",1e-3);
SetParameterDescription("classifier.libsvm.eps",
"The distance between feature vectors from the training set and "
"the fitting hyper-plane must be less than Epsilon. For outliers"
"the penalty mutliplier is set by C.");
}
}
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::TrainLibSVM(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typedef otb::LibSVMMachineLearningModel<InputValueType, OutputValueType> LibSVMType;
typename LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
libSVMClassifier->SetRegressionMode(this->m_RegressionFlag);
libSVMClassifier->SetInputListSample(trainingListSample);
libSVMClassifier->SetTargetListSample(trainingLabeledListSample);
//SVM Option
//TODO : Add other options ?
libSVMClassifier->SetParameterOptimization(GetParameterInt("classifier.libsvm.opt"));
libSVMClassifier->SetDoProbabilityEstimates(GetParameterInt("classifier.libsvm.prob"));
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libSVMClassifier->SetNu(GetParameterFloat("classifier.libsvm.nu"));
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"));
}
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