diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx index 54ecc327f6376849449f665983428527899a4c5f..783bd2b5eba098469a3a8b3ebb52bf1f77441fdf 100644 --- a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx +++ b/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx @@ -80,18 +80,18 @@ protected: Superclass::DoUpdateParameters(); } - double ComputeMSE(TargetListSampleType* list1, TargetListSampleType* list2) + double ComputeMSE(const TargetListSampleType& list1, const TargetListSampleType& list2) { - assert(list1->Size() == list2->Size()); + 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); + 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()); + mse /= static_cast<double>(list1.Size()); return mse; } @@ -109,7 +109,7 @@ protected: otbAppLogINFO("Computing training performances"); - auto mse = ComputeMSE(m_ClassificationSamplesWithLabel.labeledListSample.GetPointer(), m_PredictedList.GetPointer() ); + auto mse = ComputeMSE(*m_ClassificationSamplesWithLabel.labeledListSample, *m_PredictedList ); otbAppLogINFO("Mean Square Error = "<<mse); this->SetParameterFloat("io.mse",mse);