From 12ec94ae02597bc79d395609779c5093b7b0f526 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <cedric.traizet@c-s.fr>
Date: Thu, 9 May 2019 10:29:36 +0200
Subject: [PATCH] STY: run clang format
---
.../app/otbTrainRegression.cxx | 2 +-
.../app/otbTrainVectorClassifier.cxx | 14 ++--
.../app/otbTrainVectorRegression.cxx | 84 +++++++++----------
.../include/otbTrainVectorBase.h | 9 +-
4 files changed, 52 insertions(+), 57 deletions(-)
diff --git a/Modules/Applications/AppClassification/app/otbTrainRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
index 9012b49ab2..a93c8b5439 100644
--- a/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
+++ b/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);
}
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index c014b21b03..fbd04d4a4b 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/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
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx
index c31ebd3ad3..b5f599b629 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx
+++ b/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)
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
index 52675e935f..bc5c716aef 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
+++ b/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);
};
}
--
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