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otbTrainVectorRegression.cxx 3.67 KiB
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/*
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 * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
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 *
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 * This file is part of Orfeo Toolbox
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 *
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 *     https://www.orfeo-toolbox.org/
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 *
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 * Licensed under the Apache License, Version 2.0 (the "License");
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 * you may not use this file except in compliance with the License.
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 * You may obtain a copy of the License at
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 *
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 *     http://www.apache.org/licenses/LICENSE-2.0
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 *
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 * Unless required by applicable law or agreed to in writing, software
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 * See the License for the specific language governing permissions and
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 * limitations under the License.
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 */
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#include "otbTrainVectorBase.h"
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namespace otb
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{
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namespace Wrapper
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{
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class TrainVectorRegression : public TrainVectorBase<float, float>
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{
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public:
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  typedef TrainVectorRegression Self;
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  typedef TrainVectorBase<float, float> Superclass;
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  typedef itk::SmartPointer<Self> Pointer;
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  typedef itk::SmartPointer<const Self> ConstPointer;
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  itkNewMacro( Self )
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  itkTypeMacro( Self, Superclass )
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  typedef Superclass::SampleType SampleType;
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  typedef Superclass::ListSampleType ListSampleType;
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  typedef Superclass::TargetListSampleType TargetListSampleType;
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protected:
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  TrainVectorRegression()
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  {
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    this->m_RegressionFlag = true;
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  }
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  void DoInit() override
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  {
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    SetName( "TrainVectorRegression" );
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    SetDescription( "Train a regression algorithm based on geometries with "
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      "list of features to consider and a predictor." );
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    SetDocName( "Train Vector Regression" );
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    SetDocLongDescription( "This application trains a regression algorithm based on "
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      "a predictor geometries and a list of features to consider for "
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      "regression.\nThis application is based on LibSVM, OpenCV Machine "
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      "Learning (2.3.1 and later), and Shark ML The output of this application "
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      "is a text model file, whose format corresponds to the ML model type "
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      "chosen. There is no image nor vector data output.");
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    SetDocLimitations("");
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    SetDocAuthors( "OTB Team" );
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    SetDocSeeAlso( " " );
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    SetOfficialDocLink();
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    Superclass::DoInit();
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    AddParameter( ParameterType_Float , "io.mse" , "Mean Square Error" );
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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(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" ) ); 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.GetPointer(), m_PredictedList.GetPointer() ); otbAppLogINFO("Mean Square Error = "<<mse); this->SetParameterFloat("io.mse",mse); } private: }; } } OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorRegression )