STY: run clang format

Modules/Applications/AppClassification/app/otbTrainRegression.cxx

@@ -271,7 +271,7 @@ void ParseCSVPredictors(std::string path, ListSampleType* outputList)
       elem.Fill(0.0);
       for (unsigned int i=0 ; i<nbCols ; ++i)
         {
-        elem[i] = std::stod(words[i]);
+          elem[i] = std::stod(words[i]);
         }
       outputList->PushBack(elem);
       }

Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx

@@ -73,15 +73,13 @@ protected:
     SetOfficialDocLink();
 
     Superclass::DoInit();
-    
-    // Add a new parameter to compute confusion matrix / contingency table
-    this->AddParameter( ParameterType_OutputFilename, "io.confmatout", 
-      "Output confusion matrix or contingency table" );
-    this->SetParameterDescription( "io.confmatout", 
-      "Output file containing the confusion matrix or contingency table (.csv format)."
-      "The contingency table is output when we unsupervised algorithms is used otherwise the confusion matrix is output." );
-    this->MandatoryOff( "io.confmatout" );
 
+    // Add a new parameter to compute confusion matrix / contingency table
+    this->AddParameter(ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix or contingency table");
+    this->SetParameterDescription("io.confmatout",
+                                  "Output file containing the confusion matrix or contingency table (.csv format)."
+                                  "The contingency table is output when we unsupervised algorithms is used otherwise the confusion matrix is output.");
+    this->MandatoryOff("io.confmatout");
   }
 
   void DoUpdateParameters() override

Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx

@@ -30,14 +30,13 @@ class TrainVectorRegression : public TrainVectorBase<float, float>
 public:
   typedef TrainVectorRegression Self;
   typedef TrainVectorBase<float, float> Superclass;
-  typedef itk::SmartPointer<Self> Pointer;
+  typedef itk::SmartPointer<Self>       Pointer;
   typedef itk::SmartPointer<const Self> ConstPointer;
-  
-  itkNewMacro( Self )
-  itkTypeMacro( Self, Superclass )
 
-  typedef Superclass::SampleType SampleType;
-  typedef Superclass::ListSampleType ListSampleType;
+  itkNewMacro(Self) itkTypeMacro(Self, Superclass)
+
+      typedef Superclass::SampleType SampleType;
+  typedef Superclass::ListSampleType       ListSampleType;
   typedef Superclass::TargetListSampleType TargetListSampleType;
 
 protected:
@@ -45,79 +44,78 @@ protected:
   {
     this->m_RegressionFlag = true;
   }
-  
+
   void DoInit() override
   {
-    SetName( "TrainVectorRegression" );
-    SetDescription( "Train a regression algorithm based on geometries with "
-      "list of features to consider and a predictor." );
-
-    SetDocLongDescription( "This application trains a regression algorithm based on "
-      "a predictor geometries and a list of features to consider for "
-      "regression.\nThis application is based on LibSVM, OpenCV Machine "
-      "Learning (2.3.1 and later), and Shark ML The output of this application "
-      "is a text model file, whose format corresponds to the ML model type "
-      "chosen. There is no image nor vector data output.");
+    SetName("TrainVectorRegression");
+    SetDescription(
+        "Train a regression algorithm based on geometries with "
+        "list of features to consider and a predictor.");
+
+    SetDocLongDescription(
+        "This application trains a regression algorithm based on "
+        "a predictor geometries and a list of features to consider for "
+        "regression.\nThis application is based on LibSVM, OpenCV Machine "
+        "Learning (2.3.1 and later), and Shark ML The output of this application "
+        "is a text model file, whose format corresponds to the ML model type "
+        "chosen. There is no image nor vector data output.");
     SetDocLimitations("None");
-    SetDocAuthors( "OTB Team" );
-    SetDocSeeAlso( "TrainVectorClassifier" );
+    SetDocAuthors("OTB Team");
+    SetDocSeeAlso("TrainVectorClassifier");
 
     SetOfficialDocLink();
 
     Superclass::DoInit();
-    
-    AddParameter( ParameterType_Float , "io.mse" , "Mean Square Error" );
-    SetParameterDescription( "io.mse" ,
-      "Mean square error computed with the validation predictors" );
-    SetParameterRole( "io.mse" , Role_Output );
-    this->MandatoryOff( "io.mse" );
 
+    AddParameter(ParameterType_Float, "io.mse", "Mean Square Error");
+    SetParameterDescription("io.mse", "Mean square error computed with the validation predictors");
+    SetParameterRole("io.mse", Role_Output);
+    this->MandatoryOff("io.mse");
   }
 
   void DoUpdateParameters() override
   {
     Superclass::DoUpdateParameters();
   }
-  
+
   double ComputeMSE(const TargetListSampleType& list1, const TargetListSampleType& list2)
   {
     assert(list1.Size() == list2.Size());
     double mse = 0.;
-    for (TargetListSampleType::InstanceIdentifier i=0; i<list1.Size() ; ++i)
+    for (TargetListSampleType::InstanceIdentifier i = 0; i < list1.Size(); ++i)
     {
       auto elem1 = list1.GetMeasurementVector(i);
       auto elem2 = list2.GetMeasurementVector(i);
-      
+
       mse += (elem1[0] - elem2[0]) * (elem1[0] - elem2[0]);
     }
     mse /= static_cast<double>(list1.Size());
     return mse;
   }
-  
-  
+
+
   void DoExecute() override
   {
-    m_FeaturesInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+    m_FeaturesInfo.SetClassFieldNames(GetChoiceNames("cfield"), GetSelectedItems("cfield"));
 
-    if( m_FeaturesInfo.m_SelectedCFieldIdx.empty() && GetClassifierCategory() == Supervised )
-      {
-      otbAppLogFATAL( << "No field has been selected for data labelling!" );
-      }
+    if (m_FeaturesInfo.m_SelectedCFieldIdx.empty() && GetClassifierCategory() == Supervised)
+    {
+      otbAppLogFATAL(<< "No field has been selected for data labelling!");
+    }
 
     Superclass::DoExecute();
-    
+
     otbAppLogINFO("Computing training performances");
-    
-    auto mse = ComputeMSE(*m_ClassificationSamplesWithLabel.labeledListSample, *m_PredictedList );
 
-    otbAppLogINFO("Mean Square Error = "<<mse);
-    this->SetParameterFloat("io.mse",mse);
+    auto mse = ComputeMSE(*m_ClassificationSamplesWithLabel.labeledListSample, *m_PredictedList);
+
+    otbAppLogINFO("Mean Square Error = " << mse);
+    this->SetParameterFloat("io.mse", mse);
   }
-  
-private:
 
+private:
 };
 }
 }
 
-OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorRegression )
+OTB_APPLICATION_EXPORT(otb::Wrapper::TrainVectorRegression)

Modules/Applications/AppClassification/include/otbTrainVectorBase.h

@@ -62,8 +62,8 @@ public:
   /** Standard macro */
   itkTypeMacro(Self, Superclass);
 
-  typedef typename Superclass::SampleType SampleType;
-  typedef typename Superclass::ListSampleType ListSampleType;
+  typedef typename Superclass::SampleType           SampleType;
+  typedef typename Superclass::ListSampleType       ListSampleType;
   typedef typename Superclass::TargetListSampleType TargetListSampleType;
 
   typedef double ValueType;
@@ -87,7 +87,7 @@ protected:
   class SamplesWithLabel
   {
   public:
-    typename ListSampleType::Pointer listSample;
+    typename ListSampleType::Pointer       listSample;
     typename TargetListSampleType::Pointer labeledListSample;
     SamplesWithLabel()
     {
@@ -190,8 +190,7 @@ private:
   /**
    * Get the field of the input feature corresponding to the input field
    */
-  inline TOutputValue GetFeatureField(const ogr::Feature & feature, int field);
-
+  inline TOutputValue GetFeatureField(const ogr::Feature& feature, int field);
 };
 
 }