ENH: expose usefull parameter for LibSVM regression

Modules/Applications/AppClassification/include/otbTrainLibSVM.txx

@@ -37,6 +37,21 @@ namespace Wrapper
     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(
@@ -48,6 +63,14 @@ namespace Wrapper
     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>
@@ -91,6 +114,42 @@ namespace Wrapper
         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);
   }