diff --git a/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx b/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
index 0dd2d8f56327eaad45cae9d26101f0f1094ff31a..82d5bd8410dad7927c42fdd7a091c63f7e6d930b 100644
--- a/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
+++ b/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
@@ -37,9 +37,23 @@ namespace Wrapper
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", false);
SetParameterDescription("classifier.libsvm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Choice, "classifier.libsvm.m", "SVM Model Type");
@@ -47,21 +61,50 @@ namespace Wrapper
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", false);
+ 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", false);
}
+
AddParameter(ParameterType_Float, "classifier.libsvm.c", "Cost parameter C");
SetParameterFloat("classifier.libsvm.c",1.0, false);
- 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.");
+ 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, false);
+ 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_Empty, "classifier.libsvm.opt", "Parameters optimization");
MandatoryOff("classifier.libsvm.opt");
SetParameterDescription("classifier.libsvm.opt", "SVM parameters optimization flag.");
@@ -73,8 +116,15 @@ namespace Wrapper
{
AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
SetParameterFloat("classifier.libsvm.eps",1e-3, false);
- AddParameter(ParameterType_Float, "classifier.libsvm.nu", "Nu");
- SetParameterFloat("classifier.libsvm.nu",0.5, false);
+ 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.");
+ // AddParameter(ParameterType_Float, "classifier.libsvm.nu", "Nu");
+ // SetParameterFloat("classifier.libsvm.nu",0.5, false);
+ // SetParameterDescription("classifier.libsvm.nu",
+ // "Cost parameter Nu, in the range 0..1, the larger the value, "
+ // "the smoother the decision.");
}
}
@@ -100,6 +150,7 @@ namespace Wrapper
{
libSVMClassifier->SetDoProbabilityEstimates(true);
}
+ libSVMClassifier->SetNu(GetParameterFloat("classifier.libsvm.nu"));
libSVMClassifier->SetC(GetParameterFloat("classifier.libsvm.c"));
switch (GetParameterInt("classifier.libsvm.k"))
@@ -135,7 +186,6 @@ namespace Wrapper
break;
}
libSVMClassifier->SetEpsilon(GetParameterFloat("classifier.libsvm.eps"));
- libSVMClassifier->SetNu(GetParameterFloat("classifier.libsvm.nu"));
}
else
{
@@ -155,6 +205,7 @@ namespace Wrapper
break;
}
}
+
libSVMClassifier->Train();
libSVMClassifier->Save(modelPath);
@@ -163,4 +214,4 @@ namespace Wrapper
} //end namespace wrapper
} //end namespace otb
-#endif
+#endif
\ No newline at end of file
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
index a8baa08e58118980fff9deb9dd184e05d9ef2d0c..329722af5a2dd5d971f6dfaa0f86ef29be3dc188 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
@@ -141,7 +141,7 @@ public:
return m_Parameters.coef0;
}
- /** Set the C parameter for the training for C_SVC, EPSILON_SVR and NU_SVR */
+ /** Set the C parameter for the training for C_SVC, EPSILON_SVR and C_SVR */
otbSetSVMParameterMacro(C,C,double)
/** Get the C parameter for the training for C_SVC, EPSILON_SVR and NU_SVR */
diff --git a/Modules/Radiometry/Simulation/include/otbSoilDataBase.h b/Modules/Radiometry/Simulation/include/otbSoilDataBase.h
index 1a288b56fc55969537fd6e92d29522478f5fd934..673bc51335897306cd29140075a981db3c4be2d2 100644
--- a/Modules/Radiometry/Simulation/include/otbSoilDataBase.h
+++ b/Modules/Radiometry/Simulation/include/otbSoilDataBase.h
@@ -23,6 +23,7 @@
#define otbSoilDataBase_h
#include "OTBSimulationExport.h"
+#include "itkMacro.h"
#include <vector>
#include <unordered_map>
#include <string>
@@ -30,7 +31,7 @@
namespace otb
{
-class OTBSimulation_EXPORT SoilDataBase
+class ITK_ABI_EXPORT SoilDataBase
{
public:
//wavelength in nm
diff --git a/Modules/Radiometry/Simulation/src/otbSoilDataBase.cxx b/Modules/Radiometry/Simulation/src/otbSoilDataBase.cxx
index 124c9fbacb90b54cb11133f126a9e8bb520ec6c3..6c7686a3ecf6ce509a44d04611d0946d7950df73 100644
--- a/Modules/Radiometry/Simulation/src/otbSoilDataBase.cxx
+++ b/Modules/Radiometry/Simulation/src/otbSoilDataBase.cxx
@@ -104,7 +104,7 @@ size_t SoilDataBase::CountColumns(std::string fileName) const
}
void SoilDataBase::ParseSoilFile()
{
- unsigned int number_of_soils = CountColumns(m_SoilFileName) - 1;
+ auto number_of_soils = CountColumns(m_SoilFileName) - 1;
m_SoilDataVector.resize(number_of_soils);
std::ifstream sdb(m_SoilFileName);
if(! sdb.is_open())