ENH: Compute mse in TrainRegression

Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx

@@ -73,6 +73,22 @@ protected:
     Superclass::DoUpdateParameters();
   }
   
+  double ComputeMSE(TargetListSampleType* list1, TargetListSampleType* list2)
+  {
+    assert(list1->Size() == list2->Size());
+    double mse = 0.;
+    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" ) );
@@ -84,12 +100,11 @@ protected:
 
     Superclass::DoExecute();
     
-    /*
-    std::cout << m_PredictedList << std::endl;
-    std::cout << m_ClassificationSamplesWithLabel.labeledListSample << std::endl;
-    */
-    
+    otbAppLogINFO("Computing training performances");
     
+    auto mse = ComputeMSE(m_ClassificationSamplesWithLabel.labeledListSample.GetPointer(), m_PredictedList.GetPointer() );
+
+    otbAppLogINFO("Mean Square Error = "<<mse);
   }
   
 private: