Merge branch 'develop' into better_error_messages

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

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 */

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

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())