STYLE: naming conventions

Modules/Learning/Sampling/include/otbSampleAugmentation.h

@@ -40,7 +40,7 @@ using SampleVectorType = std::vector<SampleType>;
 Estimate standard deviations of the components in one pass using
 Welford's algorithm
 */
-SampleType estimateStds(const SampleVectorType& samples)
+SampleType EstimateStds(const SampleVectorType& samples)
 {
   const auto nbSamples = samples.size();
   const auto nbComponents = samples[0].size();
@@ -71,7 +71,7 @@ SampleType estimateStds(const SampleVectorType& samples)
 * the input samples and add them to the new data set until nbSamples
 * are added. The elements of newSamples are removed before proceeding.
 */
-void replicateSamples(const SampleVectorType& inSamples, 
+void ReplicateSamples(const SampleVectorType& inSamples, 
                       const size_t nbSamples,
                     SampleVectorType& newSamples)
 {
@@ -92,7 +92,7 @@ void replicateSamples(const SampleVectorType& inSamples,
 * input variables divided by stdFactor (defaults to 10). The
 * elements of newSamples are removed before proceeding.
 */
-void jitterSamples(const SampleVectorType& inSamples, 
+void JitterSamples(const SampleVectorType& inSamples, 
                    const size_t nbSamples,
                       SampleVectorType& newSamples,
                    float stdFactor=10,
@@ -106,7 +106,7 @@ void jitterSamples(const SampleVectorType& inSamples,
   std::srand(seed);
   // We use one gaussian distribution per component since they may
   // have different stds
-  auto stds = estimateStds(inSamples);
+  auto stds = EstimateStds(inSamples);
   std::vector<std::normal_distribution<double>> gaussDis(nbComponents);
 #pragma omp parallel for
   for(size_t i=0; i<nbComponents; ++i)
@@ -136,7 +136,7 @@ struct NeighborSorter
   }
 };
 
-double computeSquareDistance(const SampleType& x, const SampleType& y)
+double ComputeSquareDistance(const SampleType& x, const SampleType& y)
 {
   assert(x.size()==y.size());
   double dist{0};
@@ -151,7 +151,7 @@ using NNIndicesType = std::vector<NeighborType>;
 using NNVectorType = std::vector<NNIndicesType>;
 /** Returns the indices of the nearest neighbors for each input sample
 */
-void findKNNIndices(const SampleVectorType& inSamples, 
+void FindKNNIndices(const SampleVectorType& inSamples, 
                     const size_t nbNeighbors,
                     NNVectorType& nnVector)
 {
@@ -164,7 +164,7 @@ void findKNNIndices(const SampleVectorType& inSamples,
     for(size_t neighborIdx=0; neighborIdx<nbSamples; ++neighborIdx) 
       {
       if(sampleIdx!=neighborIdx)
-        nns.push_back({neighborIdx, computeSquareDistance(inSamples[sampleIdx],
+        nns.push_back({neighborIdx, ComputeSquareDistance(inSamples[sampleIdx],
                                                           inSamples[neighborIdx])});
       }  
     std::partial_sort(nns.begin(), nns.begin()+nbNeighbors, nns.end(), NeighborSorter{});
@@ -175,7 +175,7 @@ void findKNNIndices(const SampleVectorType& inSamples,
 
 /** Generate the new sample in the line linking s1 and s2
 */
-SampleType smoteCombine(const SampleType& s1, const SampleType& s2, double position)
+SampleType SmoteCombine(const SampleType& s1, const SampleType& s2, double position)
 {
   auto result = s1;
   for(size_t i=0; i<s1.size(); ++i)
@@ -189,7 +189,7 @@ synthetic minority over-sampling technique, Journal of artificial
 intelligence research, 16(), 321–357 (2002).
 http://dx.doi.org/10.1613/jair.953
 */
-void smote(const SampleVectorType& inSamples, 
+void Smote(const SampleVectorType& inSamples, 
            const size_t nbSamples,
            SampleVectorType& newSamples,
            const int nbNeighbors,
@@ -197,7 +197,7 @@ void smote(const SampleVectorType& inSamples,
 {
   newSamples.resize(nbSamples);
   NNVectorType nnVector;
-  findKNNIndices(inSamples, nbNeighbors, nnVector);
+  FindKNNIndices(inSamples, nbNeighbors, nnVector);
   // The input samples are selected randomly with replacement
   std::srand(seed);
   #pragma omp parallel for
@@ -207,7 +207,7 @@ void smote(const SampleVectorType& inSamples,
     const auto sample = inSamples[sampleIdx];
     const auto neighborIdx = nnVector[sampleIdx][std::rand()%nbNeighbors].index;
     const auto neighbor = inSamples[neighborIdx];
-    newSamples[i] = smoteCombine(sample, neighbor, std::rand()/double{RAND_MAX}); 
+    newSamples[i] = SmoteCombine(sample, neighbor, std::rand()/double{RAND_MAX}); 
     }
 }
 

Modules/Learning/Sampling/include/otbSampleAugmentationFilter.h

@@ -124,24 +124,24 @@ protected:
   using Superclass::MakeOutput;
 
 
-  SampleVectorType extractSamples(const ogr::DataSource::Pointer vectors, 
+  SampleVectorType ExtractSamples(const ogr::DataSource::Pointer vectors, 
                                   size_t layerName,
                                   const std::string& classField, const int label,
                                   const std::vector<std::string>& excludedFields = {});
 
-  void sampleToOGRFeatures(const ogr::DataSource::Pointer& vectors,
+  void SampleToOGRFeatures(const ogr::DataSource::Pointer& vectors,
                            ogr::DataSource* output, 
                            const SampleVectorType& samples,
                            const size_t layerName,
                            const std::string& classField, int label,
                            const std::vector<std::string>& excludedFields = {});
 
-  std::set<size_t> getExcludedFieldsIds(const std::vector<std::string>& excludedFields,
+  std::set<size_t> GetExcludedFieldsIds(const std::vector<std::string>& excludedFields,
                                         const ogr::Layer& inputLayer);
-bool isNumericField(const ogr::Feature& feature, const int idx);
+  bool IsNumericField(const ogr::Feature& feature, const int idx);
 
-ogr::Feature selectTemplateFeature(const ogr::Layer& inputLayer, 
-                                   const std::string& classField, int label);
+  ogr::Feature SelectTemplateFeature(const ogr::Layer& inputLayer, 
+                                     const std::string& classField, int label);
 private:
   SampleAugmentationFilter(const Self &);  //purposely not implemented
   void operator =(const Self&);      //purposely not implemented

Modules/Learning/Sampling/include/otbSampleAugmentationFilter.txx

@@ -84,7 +84,7 @@ SampleAugmentationFilter
 
   OGRDataSourcePointerType inputDS = dynamic_cast<OGRDataSourceType*>(this->itk::ProcessObject::GetInput(0));
   auto outputDS = static_cast<ogr::DataSource *>(this->itk::ProcessObject::GetOutput(0));
-  auto inSamples = this->extractSamples(inputDS, m_Layer,
+  auto inSamples = this->ExtractSamples(inputDS, m_Layer,
                                         m_ClassFieldName,
                                         m_Label,
                                         m_ExcludedFields);
@@ -93,13 +93,13 @@ SampleAugmentationFilter
     {
     case Strategy::Replicate:
     {
-    sampleAugmentation::replicateSamples(inSamples, m_NumberOfSamples,
+    sampleAugmentation::ReplicateSamples(inSamples, m_NumberOfSamples,
                                          newSamples);
     }
     break;
     case Strategy::Jitter:
     {
-    sampleAugmentation::jitterSamples(inSamples, m_NumberOfSamples,
+    sampleAugmentation::JitterSamples(inSamples, m_NumberOfSamples,
                                       newSamples,
                                       m_StdFactor,
                                       m_Seed);
@@ -107,14 +107,14 @@ SampleAugmentationFilter
     break;
     case Strategy::Smote:
     {
-    sampleAugmentation::smote(inSamples, m_NumberOfSamples,
+    sampleAugmentation::Smote(inSamples, m_NumberOfSamples,
                               newSamples,
                               m_SmoteNeighbors,
                               m_Seed);
     }
     break;
     }
-  this->sampleToOGRFeatures(inputDS, outputDS, newSamples, m_Layer,
+  this->SampleToOGRFeatures(inputDS, outputDS, newSamples, m_Layer,
                             m_ClassFieldName,
                             m_Label,
                             m_ExcludedFields);
@@ -128,7 +128,7 @@ SampleAugmentationFilter
 */
 SampleAugmentationFilter::SampleVectorType 
 SampleAugmentationFilter
-::extractSamples(const ogr::DataSource::Pointer vectors, 
+::ExtractSamples(const ogr::DataSource::Pointer vectors, 
                  size_t layerName,
                  const std::string& classField, const int label,
                  const std::vector<std::string>& excludedFields)
@@ -147,7 +147,7 @@ SampleAugmentationFilter
     }
 
   auto numberOfFields = (*featureIt).ogr().GetFieldCount();
-  auto excludedIds = this->getExcludedFieldsIds(excludedFields, layer);
+  auto excludedIds = this->GetExcludedFieldsIds(excludedFields, layer);
   SampleVectorType samples;
   int sampleCount{0};
   while( featureIt!=layer.end() )
@@ -160,7 +160,7 @@ SampleAugmentationFilter
       for(auto idx=0; idx<numberOfFields; ++idx)
         {
         if(excludedIds.find(idx) == excludedIds.cend() &&
-           this->isNumericField((*featureIt), idx))
+           this->IsNumericField((*featureIt), idx))
           mv.push_back((*featureIt).ogr().GetFieldAsDouble(idx));
         }
       samples.push_back(mv); 
@@ -178,7 +178,7 @@ SampleAugmentationFilter
 
 void 
 SampleAugmentationFilter
-::sampleToOGRFeatures(const ogr::DataSource::Pointer& vectors,
+::SampleToOGRFeatures(const ogr::DataSource::Pointer& vectors,
                       ogr::DataSource* output, 
                       const SampleAugmentationFilter::SampleVectorType& samples,
                       const size_t layerName,
@@ -187,7 +187,7 @@ SampleAugmentationFilter
 {
 
   auto inputLayer = vectors->GetLayer(layerName);
-  auto excludedIds = this->getExcludedFieldsIds(excludedFields, inputLayer);
+  auto excludedIds = this->GetExcludedFieldsIds(excludedFields, inputLayer);
 
   OGRSpatialReference * oSRS = nullptr;
   if (inputLayer.GetSpatialRef())
@@ -206,7 +206,7 @@ SampleAugmentationFilter
     }
 
   auto featureCount = outputLayer.GetFeatureCount(false);
-  auto templateFeature = this->selectTemplateFeature(inputLayer, classField, label);
+  auto templateFeature = this->SelectTemplateFeature(inputLayer, classField, label);
   for(const auto& sample : samples)
     {
     ogr::Feature dstFeature(outputLayer.GetLayerDefn());
@@ -216,7 +216,7 @@ SampleAugmentationFilter
     for (int k=0 ; k < layerDefn.GetFieldCount() ; k++)
       {
       if(excludedIds.find(k) == excludedIds.cend() &&
-         this->isNumericField(dstFeature, k))
+         this->IsNumericField(dstFeature, k))
         {
         dstFeature.ogr().SetField(k, sample[sampleFieldCounter++]);
         }
@@ -227,7 +227,7 @@ SampleAugmentationFilter
 
 std::set<size_t> 
 SampleAugmentationFilter
-::getExcludedFieldsIds(const std::vector<std::string>& excludedFields,
+::GetExcludedFieldsIds(const std::vector<std::string>& excludedFields,
                        const ogr::Layer& inputLayer)
 {
   auto feature = *(inputLayer).begin();
@@ -245,7 +245,7 @@ SampleAugmentationFilter
 
 bool 
 SampleAugmentationFilter
-::isNumericField(const ogr::Feature& feature,
+::IsNumericField(const ogr::Feature& feature,
                  const int idx)
 {
   OGRFieldType fieldType = feature.ogr().GetFieldDefnRef(idx)->GetType();
@@ -256,7 +256,7 @@ SampleAugmentationFilter
 
 ogr::Feature
 SampleAugmentationFilter
-::selectTemplateFeature(const ogr::Layer& inputLayer, 
+::SelectTemplateFeature(const ogr::Layer& inputLayer, 
                         const std::string& classField, int label)
 {
   auto wh = std::find_if(inputLayer.begin(), inputLayer.end(),