ENH: Change call to SetParameter*() function and set UserValue Flag default user value to true.

Modules/Applications/AppClassification/app/otbDSFuzzyModelEstimation.cxx

@@ -140,12 +140,12 @@ private:
     AddParameter(ParameterType_String, "cri", "Criterion");
     SetParameterDescription("cri", "Dempster Shafer criterion (by default (belief+plausibility)/2)");
     MandatoryOff("cri");
-    SetParameterString("cri","((Belief + Plausibility)/2.)");
+    SetParameterString("cri","((Belief + Plausibility)/2.)", false);
 
     AddParameter(ParameterType_Float,"wgt","Weighting");
     SetParameterDescription("wgt","Coefficient between 0 and 1 to promote undetection or false detections (default 0.5)");
     MandatoryOff("wgt");
-    SetParameterFloat("wgt", 0.5);
+    SetParameterFloat("wgt",0.5, false);
 
     AddParameter(ParameterType_InputFilename,"initmod","initialization model");
     SetParameterDescription("initmod","Initialization model (xml file) to be used. If the xml initialization model is set, the descriptor list is not used (specified using the option -desclist)");
@@ -154,12 +154,12 @@ private:
     AddParameter(ParameterType_StringList, "desclist","Descriptor list");
     SetParameterDescription("desclist","List of the descriptors to be used in the model (must be specified to perform an automatic initialization)");
     MandatoryOff("desclist");
-    SetParameterString("desclist","");
+    SetParameterString("desclist","", false);
 
     AddParameter(ParameterType_Int,"maxnbit","Maximum number of iterations");
     MandatoryOff("maxnbit");
     SetParameterDescription("maxnbit","Maximum number of optimizer iteration (default 200)");
-    SetParameterInt("maxnbit", 200);
+    SetParameterInt("maxnbit",200, false);
 
     AddParameter(ParameterType_Empty,"optobs","Optimizer Observer");
     SetParameterDescription("optobs","Activate the optimizer observer");

Modules/Applications/AppClassification/app/otbKMeansClassification.cxx

@@ -245,7 +245,7 @@ private:
         {
         otbAppLogWARNING("The available RAM is too small to process this sample size of " << GetParameterInt("ts") <<
             " pixels. The sample size will be reduced to " << maxPixNb << " pixels." << std::endl);
-        this->SetParameterInt("ts", maxPixNb);
+        this->SetParameterInt("ts",maxPixNb, false);
         }
 
       this->SetMaximumParameterIntValue("ts", maxPixNb);

Modules/Applications/AppClassification/app/otbMultiImageSamplingRate.cxx

@@ -161,7 +161,7 @@ private:
     SetParameterDescription("strategy.all","Take all samples");
 
     // Default strategy : smallest
-    SetParameterString("strategy","smallest");
+    SetParameterString("strategy","smallest", false);
 
     AddParameter(ParameterType_Choice, "mim", "Multi-Image Mode");
 

Modules/Applications/AppClassification/app/otbOGRLayerClassifier.cxx

@@ -78,7 +78,7 @@ private:
 
     AddParameter(ParameterType_String,"cfield","Field containing the predicted class.");
     SetParameterDescription("cfield","Field containing the predicted class");
-    SetParameterString("cfield","predicted");
+    SetParameterString("cfield","predicted", false);
 
     // Doc example parameter settings
     SetDocExampleParameterValue("inshp", "vectorData.shp");

Modules/Applications/AppClassification/app/otbSampleExtraction.cxx

@@ -86,7 +86,7 @@ private:
     AddParameter(ParameterType_String, "outfield.prefix.name", "Output field prefix");
     SetParameterDescription("outfield.prefix.name","Prefix used to form the field names that"
       "will contain the extracted values.");
-    SetParameterString("outfield.prefix.name", "value_");
+    SetParameterString("outfield.prefix.name", "value_", false);
 
     AddChoice("outfield.list","Use the given name list");
     SetParameterDescription("outfield.list","Use the given name list");

Modules/Applications/AppClassification/app/otbSampleSelection.cxx

@@ -197,7 +197,7 @@ private:
     SetParameterDescription("strategy.all","Take all samples");
 
     // Default strategy : smallest
-    SetParameterString("strategy","smallest");
+    SetParameterString("strategy","smallest", false);
 
     AddParameter(ParameterType_ListView, "field", "Field Name");
     SetParameterDescription("field","Name of the field carrying the class name in the input vectors.");

Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx

@@ -155,7 +155,7 @@ void DoInit() ITK_OVERRIDE
   SetParameterDescription("sample.vtr",
     "Ratio between training and validation samples (0.0 = all training, 1.0 = "
     "all validation) (default = 0.5).");
-  SetParameterFloat("sample.vtr", 0.5);
+  SetParameterFloat("sample.vtr",0.5, false);
   SetMaximumParameterFloatValue("sample.vtr",1.0);
   SetMinimumParameterFloatValue("sample.vtr",0.0);
 
@@ -214,7 +214,7 @@ void DoUpdateParameters() ITK_OVERRIDE
   if ( HasValue("io.vd") )
     {
     std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
-    GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[0]);
+    GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[0], false);
     UpdateInternalParameters("polystat");
     }
 }
@@ -289,21 +289,21 @@ void DoExecute() ITK_OVERRIDE
   for (unsigned int i=0 ; i<nbInputs ; i++)
     {
     GetInternalApplication("polystat")->SetParameterInputImage("in",imageList->GetNthElement(i));
-    GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[i]);
-    GetInternalApplication("polystat")->SetParameterString("out",polyStatTrainOutputs[i]);
+    GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[i], false);
+    GetInternalApplication("polystat")->SetParameterString("out",polyStatTrainOutputs[i], false);
     ExecuteInternal("polystat");
     // analyse polygons given for validation
     if (dedicatedValidation)
       {
-      GetInternalApplication("polystat")->SetParameterString("vec",validationVectorFileList[i]);
-      GetInternalApplication("polystat")->SetParameterString("out",polyStatValidOutputs[i]);
+      GetInternalApplication("polystat")->SetParameterString("vec",validationVectorFileList[i], false);
+      GetInternalApplication("polystat")->SetParameterString("out",polyStatValidOutputs[i], false);
       ExecuteInternal("polystat");
       }
     }
 
   // ---------------------------------------------------------------------------
   // Compute sampling rates
-  GetInternalApplication("rates")->SetParameterString("mim","proportional");
+  GetInternalApplication("rates")->SetParameterString("mim","proportional", false);
   double vtr = GetParameterFloat("sample.vtr");
   long mt = GetParameterInt("sample.mt");
   long mv = GetParameterInt("sample.mv");
@@ -348,44 +348,44 @@ void DoExecute() ITK_OVERRIDE
     }
 
   // Sampling rates for training
-  GetInternalApplication("rates")->SetParameterStringList("il",polyStatTrainOutputs);
-  GetInternalApplication("rates")->SetParameterString("out",rateTrainOut);
+  GetInternalApplication("rates")->SetParameterStringList("il",polyStatTrainOutputs, false);
+  GetInternalApplication("rates")->SetParameterString("out",rateTrainOut, false);
   if (GetParameterInt("sample.bm") != 0)
     {
-    GetInternalApplication("rates")->SetParameterString("strategy","smallest");
+    GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
     }
   else
     {
     if (fmt > -1)
       {
-      GetInternalApplication("rates")->SetParameterString("strategy","constant");
+      GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
       GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmt);
       }
     else
       {
-      GetInternalApplication("rates")->SetParameterString("strategy","all");
+      GetInternalApplication("rates")->SetParameterString("strategy","all", false);
       }
     }
   ExecuteInternal("rates");
   // Sampling rates for validation
   if (dedicatedValidation)
     {
-    GetInternalApplication("rates")->SetParameterStringList("il",polyStatValidOutputs);
-    GetInternalApplication("rates")->SetParameterString("out",rateValidOut);
+    GetInternalApplication("rates")->SetParameterStringList("il",polyStatValidOutputs, false);
+    GetInternalApplication("rates")->SetParameterString("out",rateValidOut, false);
     if (GetParameterInt("sample.bm") != 0)
       {
-      GetInternalApplication("rates")->SetParameterString("strategy","smallest");
+      GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
       }
     else
       {
       if (fmv > -1)
         {
-        GetInternalApplication("rates")->SetParameterString("strategy","constant");
+        GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
         GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmv);
         }
       else
         {
-        GetInternalApplication("rates")->SetParameterString("strategy","all");
+        GetInternalApplication("rates")->SetParameterString("strategy","all", false);
         }
       }
     ExecuteInternal("rates");
@@ -393,18 +393,18 @@ void DoExecute() ITK_OVERRIDE
 
   // ---------------------------------------------------------------------------
   // Select & extract samples
-  GetInternalApplication("select")->SetParameterString("sampler", "periodic");
+  GetInternalApplication("select")->SetParameterString("sampler", "periodic", false);
   GetInternalApplication("select")->SetParameterInt("sampler.periodic.jitter",50);
-  GetInternalApplication("select")->SetParameterString("strategy","byclass");
-  GetInternalApplication("extraction")->SetParameterString("outfield", "prefix");
-  GetInternalApplication("extraction")->SetParameterString("outfield.prefix.name","value_");
+  GetInternalApplication("select")->SetParameterString("strategy","byclass", false);
+  GetInternalApplication("extraction")->SetParameterString("outfield", "prefix", false);
+  GetInternalApplication("extraction")->SetParameterString("outfield.prefix.name","value_", false);
   for (unsigned int i=0 ; i<nbInputs ; i++)
     {
     GetInternalApplication("select")->SetParameterInputImage("in",imageList->GetNthElement(i));
-    GetInternalApplication("select")->SetParameterString("vec",vectorFileList[i]);
-    GetInternalApplication("select")->SetParameterString("out",sampleOutputs[i]);
-    GetInternalApplication("select")->SetParameterString("instats",polyStatTrainOutputs[i]);
-    GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesTrainOutputs[i]);
+    GetInternalApplication("select")->SetParameterString("vec",vectorFileList[i], false);
+    GetInternalApplication("select")->SetParameterString("out",sampleOutputs[i], false);
+    GetInternalApplication("select")->SetParameterString("instats",polyStatTrainOutputs[i], false);
+    GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesTrainOutputs[i], false);
     // select sample positions
     ExecuteInternal("select");
     // extract sample descriptors
@@ -412,10 +412,10 @@ void DoExecute() ITK_OVERRIDE
 
     if (dedicatedValidation)
       {
-      GetInternalApplication("select")->SetParameterString("vec",validationVectorFileList[i]);
-      GetInternalApplication("select")->SetParameterString("out",sampleValidOutputs[i]);
-      GetInternalApplication("select")->SetParameterString("instats",polyStatValidOutputs[i]);
-      GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesValidOutputs[i]);
+      GetInternalApplication("select")->SetParameterString("vec",validationVectorFileList[i], false);
+      GetInternalApplication("select")->SetParameterString("out",sampleValidOutputs[i], false);
+      GetInternalApplication("select")->SetParameterString("instats",polyStatValidOutputs[i], false);
+      GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesValidOutputs[i], false);
       // select sample positions
       ExecuteInternal("select");
       // extract sample descriptors
@@ -473,8 +473,8 @@ void DoExecute() ITK_OVERRIDE
 
   // ---------------------------------------------------------------------------
   // Train model
-  GetInternalApplication("training")->SetParameterStringList("io.vd",sampleTrainOutputs);
-  GetInternalApplication("training")->SetParameterStringList("valid.vd",sampleValidOutputs);
+  GetInternalApplication("training")->SetParameterStringList("io.vd",sampleTrainOutputs, false);
+  GetInternalApplication("training")->SetParameterStringList("valid.vd",sampleValidOutputs, false);
   UpdateInternalParameters("training");
   // set field names
   FloatVectorImageType::Pointer image = imageList->GetNthElement(0);
@@ -486,7 +486,7 @@ void DoExecute() ITK_OVERRIDE
     oss << i;
     selectedNames.push_back("value_"+oss.str());
     }
-  GetInternalApplication("training")->SetParameterStringList("feat",selectedNames);
+  GetInternalApplication("training")->SetParameterStringList("feat",selectedNames, false);
   ExecuteInternal("training");
 
   // cleanup

Modules/Applications/AppClassification/app/otbTrainOGRLayersClassifier.cxx

@@ -80,7 +80,7 @@ private:
 
     AddParameter(ParameterType_String,"cfield","Field containing the class id for supervision");
     SetParameterDescription("cfield","Field containing the class id for supervision. Only geometries with this field available will be taken into account.");
-    SetParameterString("cfield","class");
+    SetParameterString("cfield","class", false);
 
     // Doc example parameter settings
     SetDocExampleParameterValue("inshp", "vectorData.shp");

Modules/Applications/AppClassification/app/otbTrainRegression.cxx

@@ -159,7 +159,7 @@ void DoInit() ITK_OVERRIDE
   AddParameter(ParameterType_Float, "sample.vtr", "Training and validation sample ratio");
   SetParameterDescription("sample.vtr",
                           "Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).");
-  SetParameterFloat("sample.vtr", 0.5);
+  SetParameterFloat("sample.vtr",0.5, false);
 
   Superclass::DoInit();
 

Modules/Applications/AppClassification/app/otbVectorDataDSValidation.cxx

@@ -84,12 +84,12 @@ private:
     AddParameter(ParameterType_String, "cri", "Criterion");
     SetParameterDescription("cri", "Dempster Shafer criterion (by default (belief+plausibility)/2)");
     MandatoryOff("cri");
-    SetParameterString("cri", "((Belief + Plausibility)/2.)");
+    SetParameterString("cri", "((Belief + Plausibility)/2.)", false);
 
     AddParameter(ParameterType_Float, "thd", "Criterion threshold");
     SetParameterDescription("thd", "Criterion threshold (default 0.5)");
     MandatoryOff("thd");
-    SetParameterFloat("thd", 0.5);
+    SetParameterFloat("thd",0.5, false);
 
     AddParameter(ParameterType_OutputVectorData, "out", "Output Vector Data");
     SetParameterDescription("out", "Output VectorData containing only the validated samples");

Modules/Applications/AppClassification/include/otbTrainBoost.txx

@@ -39,22 +39,22 @@ namespace Wrapper
     AddChoice("classifier.boost.t.logit", "LogitBoost (technique producing good regression fits)");
     AddChoice("classifier.boost.t.gentle", "Gentle AdaBoost (technique setting less weight on outlier data points "
                                                "and, for that reason, being often good with regression data)");
-    SetParameterString("classifier.boost.t", "real");
+    SetParameterString("classifier.boost.t", "real", false);
     SetParameterDescription("classifier.boost.t", "Type of Boosting algorithm.");
     //Do not expose SplitCriteria
     //WeakCount
     AddParameter(ParameterType_Int, "classifier.boost.w", "Weak count");
-    SetParameterInt("classifier.boost.w", 100);
+    SetParameterInt("classifier.boost.w",100, false);
     SetParameterDescription("classifier.boost.w","The number of weak classifiers.");
     //WeightTrimRate
     AddParameter(ParameterType_Float, "classifier.boost.r", "Weight Trim Rate");
-    SetParameterFloat("classifier.boost.r", 0.95);
+    SetParameterFloat("classifier.boost.r",0.95, false);
     SetParameterDescription("classifier.boost.r","A threshold between 0 and 1 used to save computational time. "
                             "Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. "
                             "Set this parameter to 0 to turn off this functionality.");
     //MaxDepth : Not sure that this parameter has to be exposed.
     AddParameter(ParameterType_Int, "classifier.boost.m", "Maximum depth of the tree");
-    SetParameterInt("classifier.boost.m", 1);
+    SetParameterInt("classifier.boost.m",1, false);
     SetParameterDescription("classifier.boost.m","Maximum depth of the tree.");
   }
 

Modules/Applications/AppClassification/include/otbTrainDecisionTree.txx

@@ -34,7 +34,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
                           "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/decision_trees.html}.");
   //MaxDepth
   AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
-  SetParameterInt("classifier.dt.max", 65535);
+  SetParameterInt("classifier.dt.max",65535, false);
   SetParameterDescription(
       "classifier.dt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
       "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "
@@ -42,13 +42,13 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
   //MinSampleCount
   AddParameter(ParameterType_Int, "classifier.dt.min", "Minimum number of samples in each node");
-  SetParameterInt("classifier.dt.min", 10);
+  SetParameterInt("classifier.dt.min",10, false);
   SetParameterDescription("classifier.dt.min", "If the number of samples in a node is smaller than this parameter, "
                           "then this node will not be split.");
 
   //RegressionAccuracy
   AddParameter(ParameterType_Float, "classifier.dt.ra", "Termination criteria for regression tree");
-  SetParameterFloat("classifier.dt.ra", 0.01);
+  SetParameterFloat("classifier.dt.ra",0.01, false);
   SetParameterDescription("classifier.dt.min", "If all absolute differences between an estimated value in a node "
                           "and the values of the train samples in this node are smaller than this regression accuracy parameter, "
                           "then the node will not be split.");
@@ -60,14 +60,14 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //MaxCategories
   AddParameter(ParameterType_Int, "classifier.dt.cat",
                "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split");
-  SetParameterInt("classifier.dt.cat", 10);
+  SetParameterInt("classifier.dt.cat",10, false);
   SetParameterDescription(
       "classifier.dt.cat",
       "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.");
 
   //CVFolds
   AddParameter(ParameterType_Int, "classifier.dt.f", "K-fold cross-validations");
-  SetParameterInt("classifier.dt.f", 10);
+  SetParameterInt("classifier.dt.f",10, false);
   SetParameterDescription(
       "classifier.dt.f", "If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds.");
 

Modules/Applications/AppClassification/include/otbTrainGradientBoostedTree.txx

@@ -45,7 +45,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
   //WeakCount
   AddParameter(ParameterType_Int, "classifier.gbt.w", "Number of boosting algorithm iterations");
-  SetParameterInt("classifier.gbt.w", 200);
+  SetParameterInt("classifier.gbt.w",200, false);
   SetParameterDescription(
       "classifier.gbt.w",
       "Number \"w\" of boosting algorithm iterations, with w*K being the total number of trees in "
@@ -53,20 +53,20 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
   //Shrinkage
   AddParameter(ParameterType_Float, "classifier.gbt.s", "Regularization parameter");
-  SetParameterFloat("classifier.gbt.s", 0.01);
+  SetParameterFloat("classifier.gbt.s",0.01, false);
   SetParameterDescription("classifier.gbt.s", "Regularization parameter.");
 
   //SubSamplePortion
   AddParameter(ParameterType_Float, "classifier.gbt.p",
                "Portion of the whole training set used for each algorithm iteration");
-  SetParameterFloat("classifier.gbt.p", 0.8);
+  SetParameterFloat("classifier.gbt.p",0.8, false);
   SetParameterDescription(
       "classifier.gbt.p",
       "Portion of the whole training set used for each algorithm iteration. The subset is generated randomly.");
 
   //MaxDepth
   AddParameter(ParameterType_Int, "classifier.gbt.max", "Maximum depth of the tree");
-  SetParameterInt("classifier.gbt.max", 3);
+  SetParameterInt("classifier.gbt.max",3, false);
   SetParameterDescription(
         "classifier.gbt.max", "The training algorithm attempts to split each node while its depth is smaller than the maximum "
         "possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or "

Modules/Applications/AppClassification/include/otbTrainKNN.txx

@@ -34,7 +34,7 @@ namespace Wrapper
 
     //K parameter
     AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
-    SetParameterInt("classifier.knn.k", 32);
+    SetParameterInt("classifier.knn.k",32, false);
     SetParameterDescription("classifier.knn.k","The number of neighbors to use.");
 
     if (this->m_RegressionFlag)

Modules/Applications/AppClassification/include/otbTrainLibSVM.txx

@@ -35,7 +35,7 @@ namespace Wrapper
     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");
+    SetParameterString("classifier.libsvm.k", "linear", false);
     SetParameterDescription("classifier.libsvm.k", "SVM Kernel Type.");
     AddParameter(ParameterType_Choice, "classifier.libsvm.m", "SVM Model Type");
     SetParameterDescription("classifier.libsvm.m", "Type of SVM formulation.");
@@ -43,17 +43,17 @@ namespace Wrapper
       {
       AddChoice("classifier.libsvm.m.epssvr", "Epsilon Support Vector Regression");
       AddChoice("classifier.libsvm.m.nusvr", "Nu Support Vector Regression");
-      SetParameterString("classifier.libsvm.m", "epssvr");
+      SetParameterString("classifier.libsvm.m", "epssvr", false);
       }
     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");
+      SetParameterString("classifier.libsvm.m", "csvc", false);
       }
     AddParameter(ParameterType_Float, "classifier.libsvm.c", "Cost parameter C");
-    SetParameterFloat("classifier.libsvm.c", 1.0);
+    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.");
@@ -67,9 +67,9 @@ namespace Wrapper
     if (this->m_RegressionFlag)
       {
       AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
-      SetParameterFloat("classifier.libsvm.eps", 1e-3);
+      SetParameterFloat("classifier.libsvm.eps",1e-3, false);
       AddParameter(ParameterType_Float, "classifier.libsvm.nu", "Nu");
-      SetParameterFloat("classifier.libsvm.nu", 0.5);
+      SetParameterFloat("classifier.libsvm.nu",0.5, false);
       }
   }
 

Modules/Applications/AppClassification/include/otbTrainNeuralNetwork.txx

@@ -38,7 +38,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   AddParameter(ParameterType_Choice, "classifier.ann.t", "Train Method Type");
   AddChoice("classifier.ann.t.reg", "RPROP algorithm");
   AddChoice("classifier.ann.t.back", "Back-propagation algorithm");
-  SetParameterString("classifier.ann.t", "reg");
+  SetParameterString("classifier.ann.t", "reg", false);
   SetParameterDescription("classifier.ann.t", "Type of training method for the multilayer perceptron (MLP) neural network.");
 
   //LayerSizes
@@ -56,25 +56,25 @@ LearningApplicationBase<TInputValue,TOutputValue>
   AddChoice("classifier.ann.f.ident", "Identity function");
   AddChoice("classifier.ann.f.sig", "Symmetrical Sigmoid function");
   AddChoice("classifier.ann.f.gau", "Gaussian function (Not completely supported)");
-  SetParameterString("classifier.ann.f", "sig");
+  SetParameterString("classifier.ann.f", "sig", false);
   SetParameterDescription("classifier.ann.f", "Neuron activation function.");
 
   //Alpha
   AddParameter(ParameterType_Float, "classifier.ann.a", "Alpha parameter of the activation function");
-  SetParameterFloat("classifier.ann.a", 1.);
+  SetParameterFloat("classifier.ann.a",1., false);
   SetParameterDescription("classifier.ann.a",
                           "Alpha parameter of the activation function (used only with sigmoid and gaussian functions).");
 
   //Beta
   AddParameter(ParameterType_Float, "classifier.ann.b", "Beta parameter of the activation function");
-  SetParameterFloat("classifier.ann.b", 1.);
+  SetParameterFloat("classifier.ann.b",1., false);
   SetParameterDescription("classifier.ann.b",
                           "Beta parameter of the activation function (used only with sigmoid and gaussian functions).");
 
   //BackPropDWScale
   AddParameter(ParameterType_Float, "classifier.ann.bpdw",
                "Strength of the weight gradient term in the BACKPROP method");
-  SetParameterFloat("classifier.ann.bpdw", 0.1);
+  SetParameterFloat("classifier.ann.bpdw",0.1, false);
   SetParameterDescription(
       "classifier.ann.bpdw",
       "Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1.");
@@ -82,7 +82,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //BackPropMomentScale
   AddParameter(ParameterType_Float, "classifier.ann.bpms",
                "Strength of the momentum term (the difference between weights on the 2 previous iterations)");
-  SetParameterFloat("classifier.ann.bpms", 0.1);
+  SetParameterFloat("classifier.ann.bpms",0.1, false);
   SetParameterDescription(
       "classifier.ann.bpms",
       "Strength of the momentum term (the difference between weights on the 2 previous iterations). "
@@ -92,12 +92,12 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //RegPropDW0
   AddParameter(ParameterType_Float, "classifier.ann.rdw",
                "Initial value Delta_0 of update-values Delta_{ij} in RPROP method");
-  SetParameterFloat("classifier.ann.rdw", 0.1);
+  SetParameterFloat("classifier.ann.rdw",0.1, false);
   SetParameterDescription("classifier.ann.rdw", "Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1).");
 
   //RegPropDWMin
   AddParameter(ParameterType_Float, "classifier.ann.rdwm", "Update-values lower limit Delta_{min} in RPROP method");
-  SetParameterFloat("classifier.ann.rdwm", 1e-7);
+  SetParameterFloat("classifier.ann.rdwm",1e-7, false);
   SetParameterDescription(
       "classifier.ann.rdwm",
       "Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7).");
@@ -107,18 +107,18 @@ LearningApplicationBase<TInputValue,TOutputValue>
   AddChoice("classifier.ann.term.iter", "Maximum number of iterations");
   AddChoice("classifier.ann.term.eps", "Epsilon");
   AddChoice("classifier.ann.term.all", "Max. iterations + Epsilon");
-  SetParameterString("classifier.ann.term", "all");
+  SetParameterString("classifier.ann.term", "all", false);
   SetParameterDescription("classifier.ann.term", "Termination criteria.");
 
   //Epsilon
   AddParameter(ParameterType_Float, "classifier.ann.eps", "Epsilon value used in the Termination criteria");
-  SetParameterFloat("classifier.ann.eps", 0.01);
+  SetParameterFloat("classifier.ann.eps",0.01, false);
   SetParameterDescription("classifier.ann.eps", "Epsilon value used in the Termination criteria.");
 
   //MaxIter
   AddParameter(ParameterType_Int, "classifier.ann.iter",
                "Maximum number of iterations used in the Termination criteria");
-  SetParameterInt("classifier.ann.iter", 1000);
+  SetParameterInt("classifier.ann.iter",1000, false);
   SetParameterDescription("classifier.ann.iter", "Maximum number of iterations used in the Termination criteria.");
 
 }

Modules/Applications/AppClassification/include/otbTrainRandomForests.txx

@@ -34,7 +34,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
                           "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/random_trees.html}.");
   //MaxDepth
   AddParameter(ParameterType_Int, "classifier.rf.max", "Maximum depth of the tree");
-  SetParameterInt("classifier.rf.max", 5);
+  SetParameterInt("classifier.rf.max",5, false);
   SetParameterDescription(
       "classifier.rf.max",
       "The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. "
@@ -42,14 +42,14 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
   //MinSampleCount
   AddParameter(ParameterType_Int, "classifier.rf.min", "Minimum number of samples in each node");
-  SetParameterInt("classifier.rf.min", 10);
+  SetParameterInt("classifier.rf.min",10, false);
   SetParameterDescription(
       "classifier.rf.min", "If the number of samples in a node is smaller than this parameter, "
       "then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.");
 
   //RegressionAccuracy
   AddParameter(ParameterType_Float, "classifier.rf.ra", "Termination Criteria for regression tree");
-  SetParameterFloat("classifier.rf.ra", 0.);
+  SetParameterFloat("classifier.rf.ra",0., false);
   SetParameterDescription("classifier.rf.ra", "If all absolute differences between an estimated value in a node "
                           "and the values of the train samples in this node are smaller than this regression accuracy parameter, "
                           "then the node will not be split.");
@@ -61,7 +61,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //MaxNumberOfCategories
   AddParameter(ParameterType_Int, "classifier.rf.cat",
                "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split");
-  SetParameterInt("classifier.rf.cat", 10);
+  SetParameterInt("classifier.rf.cat",10, false);
   SetParameterDescription(
       "classifier.rf.cat",
       "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.");
@@ -73,7 +73,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //MaxNumberOfVariables
   AddParameter(ParameterType_Int, "classifier.rf.var",
                "Size of the randomly selected subset of features at each tree node");
-  SetParameterInt("classifier.rf.var", 0);
+  SetParameterInt("classifier.rf.var",0, false);
   SetParameterDescription(
       "classifier.rf.var",
       "The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). "
@@ -82,7 +82,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //MaxNumberOfTrees
   AddParameter(ParameterType_Int, "classifier.rf.nbtrees",
                "Maximum number of trees in the forest");
-  SetParameterInt("classifier.rf.nbtrees", 100);
+  SetParameterInt("classifier.rf.nbtrees",100, false);
   SetParameterDescription(
       "classifier.rf.nbtrees",
       "The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. "
@@ -92,7 +92,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
   //ForestAccuracy
   AddParameter(ParameterType_Float, "classifier.rf.acc",
                "Sufficient accuracy (OOB error)");
-  SetParameterFloat("classifier.rf.acc", 0.01);
+  SetParameterFloat("classifier.rf.acc",0.01, false);
   SetParameterDescription("classifier.rf.acc","Sufficient accuracy (OOB error).");
 
 

Modules/Applications/AppClassification/include/otbTrainSVM.txx

@@ -37,35 +37,35 @@ namespace Wrapper
       {
       AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
       AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
-      SetParameterString("classifier.svm.m", "epssvr");
+      SetParameterString("classifier.svm.m", "epssvr", false);
       }
     else
       {
       AddChoice("classifier.svm.m.csvc", "C support vector classification");
       AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
       AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
-      SetParameterString("classifier.svm.m", "csvc");
+      SetParameterString("classifier.svm.m", "csvc", false);
       }
     AddParameter(ParameterType_Choice, "classifier.svm.k", "SVM Kernel Type");
     AddChoice("classifier.svm.k.linear", "Linear");
     AddChoice("classifier.svm.k.rbf", "Gaussian radial basis function");
     AddChoice("classifier.svm.k.poly", "Polynomial");
     AddChoice("classifier.svm.k.sigmoid", "Sigmoid");
-    SetParameterString("classifier.svm.k", "linear");
+    SetParameterString("classifier.svm.k", "linear", false);
     SetParameterDescription("classifier.svm.k", "SVM Kernel Type.");
     AddParameter(ParameterType_Float, "classifier.svm.c", "Cost parameter C");
-    SetParameterFloat("classifier.svm.c", 1.0);
+    SetParameterFloat("classifier.svm.c",1.0, false);
     SetParameterDescription(
         "classifier.svm.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.svm.nu",
                  "Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS)");
-    SetParameterFloat("classifier.svm.nu", 0.0);
+    SetParameterFloat("classifier.svm.nu",0.0, false);
     SetParameterDescription("classifier.svm.nu", "Parameter nu of a SVM optimization problem.");
     if (this->m_RegressionFlag)
       {
       AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR)");
-      SetParameterFloat("classifier.svm.p", 1.0);
+      SetParameterFloat("classifier.svm.p",1.0, false);
       SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
 
       AddParameter(ParameterType_Choice, "classifier.svm.term", "Termination criteria");
@@ -75,22 +75,22 @@ namespace Wrapper
       AddChoice("classifier.svm.term.all", "Stops when either iteration or epsilon criteria is true");
 
       AddParameter(ParameterType_Float, "classifier.svm.iter", "Maximum iteration");
-      SetParameterFloat("classifier.svm.iter", 1000);
+      SetParameterFloat("classifier.svm.iter",1000, false);
       SetParameterDescription("classifier.svm.iter", "Maximum number of iterations (corresponds to the termination criteria 'iter').");
 
       AddParameter(ParameterType_Float, "classifier.svm.eps", "Epsilon accuracy threshold");
-      SetParameterFloat("classifier.svm.eps", FLT_EPSILON);
+      SetParameterFloat("classifier.svm.eps",FLT_EPSILON, false);
       SetParameterDescription("classifier.svm.eps", "Epsilon accuracy (corresponds to the termination criteria 'eps').");
       }
     AddParameter(ParameterType_Float, "classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID)");
-    SetParameterFloat("classifier.svm.coef0", 0.0);
+    SetParameterFloat("classifier.svm.coef0",0.0, false);
     SetParameterDescription("classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
     AddParameter(ParameterType_Float, "classifier.svm.gamma",
                  "Parameter gamma of a kernel function (POLY / RBF / SIGMOID)");
-    SetParameterFloat("classifier.svm.gamma", 1.0);
+    SetParameterFloat("classifier.svm.gamma",1.0, false);
     SetParameterDescription("classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
     AddParameter(ParameterType_Float, "classifier.svm.degree", "Parameter degree of a kernel function (POLY)");
-    SetParameterFloat("classifier.svm.degree", 1.0);